Data engineer vs software engineer reddit

Data engineer vs software engineer reddit. You can use that as a stepping stone to a software engineer job too. While data analytics focuses on deriving insights from data, software engineering concentrates on building and optimizing software systems. • 1 yr. Together, these fields play critical roles Software Engineer (data platform) - a software engineer who work on data related products. Backend software engineer salary. Also, data jobs require more business knowledge. But only because we have 6 DEs in the whole firm and too many software devs. There are more people applying to software engineer jobs than data engineer jobs. It can also lead to higher paying roles like solutions architect and sales engineer. Their coding DEs are known as “Software engineer - Data” or simple software engineers. AEs don't necessarily need to switch jobs every 18 months to make Regular software eng won’t have much actual math. They probably want to hire you to make software to aid in financial analysis (data mining, data fitting, some AI, etc. 3. Like a regional retail chain in the Midwest will likely hire one or a few devs for like $70k, bringing the average down. It is an overloaded term, so it depends. In fact, at least for me, Data Engineering is easier than software engineering, it's just moving data from At my company there is probably a 20:1 ratio of software engineers to data engineers. Both pay well, but I think that data science jobs are more competitive and thus pay more. In reality, they may not care that much about the differences in engineer definitions. The focus is on efficiency within the system. This was at Google and Microsoft. Almost all companies need data engineers. Imo data engineering is a bit easier but obviously it depends on the team and work. Lastly, please don’t base your career path solely on how much money you will make, you may find yourself devops is all about automating operations, such as configuring servers, installing and updating applications, monitoring and reacting. In summary, DE is will be attractive to an employer as someone who can potentially make the business more money, whereas DevOps is perceived as a cost, but both are incredibly valuable to any organisation, you’ll have to look hard. I consider myself a software engineer first, and data engineering as a specialized area. The best data engineers that I have seen are very good software engineers. One could say that while software engineering goes wide, software development goes deep. I do see that difference in the field and it also exists on most data science teams, not just bioinformatics. net systems. From what I know, and don't take this as gospel, Data Science is more math/statistics based coding/software design. Developers with a full stack have the technical know-how to work on both parts of a project. This role is more about building and maintaining the infrastructure that allows data to be used effectively, rather than analyzing the data itself. It's a frustrating dynamic. Sometimes your insights aren’t valuable or your data just sucks and you don’t feel important to your company. e setting up infrastructure, scaling databases, replicating data, working with Spark and so on. Growth is the same. Business Data Engineer (Title: Data Engineer or Analytics Engineer): Solves business problems by building data pipelines and data models related to 3rd party systems and product data. Data engineering is a specialisation of software engineering. a data engineer like any engineer queries a DB on occasion. But I guess you could say that data engineers are the back-end developers of "data-intensive application development". Hi! I'm currently a Software Engineer (1. 5. I'm not saying don't attend university if you intend to work in this field, you want to stay competitive and the reality is most comp sci / software engineers have I can hope to make $150k or so within 3-5 years. In contrast, Controls Engineering focuses on designing and managing control systems used in manufacturing, production, and other The other thing I noticed is how data engineers try to solve problems differs from software engineers. DEs are basically software engineers who works mostly on data to put it simply. dataschlepper. Software engineers usually just make changes to big websites like YouTube, Amazon, Twitter, and Facebook, but these big websites can hire data engineers as well. I would, however, offer that Data Engineer sounds like a specific type of Software Engineer focused on information systems and database architecture. Data Engineer. I was a software engineer for five years working across a variety of. If you enjoy coding more, do ML Engineer. On the other hand, DE will still be needing to process the raw/lake data for data scientists. this is a gross over generalization. I spent a summer as a Data Scientist intern and now work as ML Engineer. data engineering is about automating the collection and transformation into an easily analysable form. If I had these options, I would probably take the software engineering course and use the project to do something with some of the DE tools like Spark and a cloud service. I would pick Software Engineer as it has the best career growth opportunities. Yes, DEs at FAANG are more about sql. Cons: problems are more open-ended. Specialize in data, but know devops and you will always have a job. This is because there are more junior roles available, higher salaries, and a more standardized career structure. The key metric for my team is to enhance the efficiency of other teams, rather than just increasing user screen time I worked a bit in software development and then fell into data engineering (small scale) by chance. Or don’t accept the offer and focus on finding SE role. Most ML Engineers I've met come from having Software Data engineer salary vs. Can be hardware, software or even industrial process. To me, if you don't have the SWE tools and mindset, you're an analyst. And probably better paying, too. Cybersec is just as crucial to the business as DE, but not in the eyes of managers and directors. If all you want is 10000 job opportunities then JavaScript / PHP have you covered. One (usually) has to be able to consistently produce releasable, saleable, commercial software the other (usually) does not. Some additions. My current pay is above the $80k initial salary by almost 20%. If you are referring to needing to know how servers work or REST APIs or browsers, etc, then you don’t need all that knowledge to be a DE. • 10 mo. u can see the convergence in tooling too - elastic, clickhouse - these are used both by devops and data eng teams. Software engineering is a huge field, and it includes things like: Data engineer is just specialized software engineer. If you’re talking about analytics data engineering (i. Data Analyst with 7 years experience worked as a contractor, for big insurance, and now public sector. 5 YOE) in a product team in the insurance field, and was reached out by Datadog for an IT Systems Engineering role. For maybe five of those, I was doing significant amounts of data analysis, and working with people whose full time job was data engineering. To move to DE as a software engineer, you will need to understand how frameworks (Spark, Flink) work. do your best to acquire both skills. In simplest terms, software engineering involves the development of software, while data science deals with analyzing, scrubbing and presenting data in order to help solve a real-life problem or within a specific organization, using statistics, programming as well as the business aspect. which helped hone these skills (and was comp science degree/data science major). But for data engineering interviews, I've noticed that the questions ask easier or moderately difficult Python questions involving string manipulations or dictionaries, followed by more difficult SQL questions. Data Engineering as a domain is divided into 2 major parts: with the focus on DWH/SQL and with the focus on software engineering. Not all companies have a devops presence. TLDR: The best way to move up in your career in data science is to become a software engineer. Reply reply. Typically depend heavily on Fivetran, SQL and dbt to do most of their work and will generally be in a lot of meetings. If you're doing software engineering with the job title "data engineer" then yes. I'm a Junior in college with a decent amount of experience in Software Engineering; I've been developing for the past 4 years, interned as a Software Engineer for 3 summers, and generally have a big-picture, production-level approach when working on freelance and side projects (primarily backend development). The other thing I noticed is how data engineers try to solve problems differs from software engineers. Title says it. Duties will vary based on employer but as far as what I use it’s mostly SQL, ETL tools, R or Python and data visualization and business intelligence tools. Data scientists are expected to write good code but the focus isn’t on working software, it’s on insights. On average I'd say software engineering is stressful though. r/engineering is a forum for engineering professionals to share information, knowledge, experience related to the principles & practices of the numerous engineering disciplines. SW engineering encompasses a broad range of sub-disciplines, including data science. I personally prefer software engineering as a career. Can use anything from JavaScript to C++. The software engineers are who use code to bring that data from a database all the way to its end destination that could be for customers or internal tools idk. Finally there’s the blue collar tech side, those are the data center techs and field technicians. The DS internship lasts 3 months in the summer with no certainty of full-time offer after. a data engineer can be closer towards a data scientist and ultimately slap some API on top of some system involving data or it can be more towards a generalist but with an ETL flair. So that might make the competition a little harder for any specific job for software engineers but you have many more jobs to apply to If you’re talking about data Infrastructure engineering, that’s basically backend engineering. Yup. Also, from a supply-and-demand POV, it would seem like DEs would have higher salaries since they're in shorter supply (and Data engineer is gonna be focused on data pipelines, sql, and ETL to manipulate data in databases. Unless I transition to Data science which I can with my stem degree and experience. So the need for tight devop experience increases as well. If you want to build things with code, go be a data engineer. Engineers/developers is a growing profession and a lot of companies are increasing those positions. icamehereyesterdie. Reply. Weesy02. There are certainly low stress jobs out there where deadlines are loose and the software isn't mission critical. r/engineering is **NOT** for students to ask for guidance on selecting their major, or for homework / project help. If it's under 2-3 years or there are fewer than 3-5 other data engineers/ML engineers, it's bait & switch and you will be doing bitch work. If I went down the IT consulting route . Reply reply 6. Can work with both frontend or backend. So I am a degree apprentice and I have two options for my four year employment. Companies that need data engineers are likely in higher paying industries, like tech tech and finance. The reason I'm really considering the internship is because data science taps more into my astrophysics knowledge and If you want to build in PowerPoint and hope things work, go be an architect. They collaborate with stakeholders to understand requirements, architect software systems, write code, and ensure the reliability and performance of applications. If coding is super important for you, you can accept the offer and try to switch the teams when you are at the company. reddit's new API changes kill third party apps that offer accessibility features, mod tools, and other features not found in the first party app. If you end up hating data engineering, then you are kind of stuck between a rock and a hard place but SWE is less specialized, which is nice early in a career. I loved the switch. Then I did “data engineering” which really was a mix of etl, backend development, data pipelining , MLE, and some ops type of work for the rest of my tenure. Data engineering is a derived career path from software engineering. According-Benefit-12. But for SDE, one can expect work in frontend, backend or data part itself. rexicusmaximus. 13. Data engineering is still not bad and could be easier to transition with your current experience. A good idea is to figure out how many data engineers/ML engineers they already have and for how long they have been working in those positions. My first employer I interviewed as a SWE, was put on a data engineering team and did backend dev for 6 months. I'm deciding between a full-time SE role and a data science intern role, really impressed by both companies, comparable base pay. Software Engineers are paid much more than any data scientist/data analyst role. I then got an opportunity to join a new organization, exclusively as a data engineer. On the other hand, data engineers might earn up to $116,000 per year. This means you have the flexibility of switching between both career paths however with some additional learning depending on the route you chose. Software development is more focused on the actual algorithm development and lower level testing. tdatas. Now, I have a hard time getting interviews for a software position since I realized that I like developing tools more than writing simple scripts and run some queries in a database. Data analyst doesn’t get paid that much and beyond. Ancient_Pace7614. If you don't write tests, it is a high stress job. So being a Junior software engineer would mean I could take a significant pay cut. Edit: added words. Software engineers coming to data engineering are usually the key offenders when it comes to lack of data modelling knowledge, so having strong knowledge of BI practices will help set you apart (although to be fair, getting your masters in comp sci at 40+ would be enough to make me take notice that you know a thing or two about learning new stuff) More impactful on a platform level: Rather than being constantly driven by the "feature after feature after feature" mindset, I am now focused on having a multiplicative impact on other teams (such as analytics and data science). Every statistician, physics, CS, engineering or quant heavy graduates are trying to get into DS, which just If I could go back in time I would study something else tbh. The other things I noted were salary and what some other developers opinions. It's because most haven't been mentored for proper software design. Also a BSWE gets to work on fun biology problems, while as a SWE you may get asked to sync data between Salesforce and a MongoDB database. The role describes itself as an internal Software Engineering role, but instead of developing products I'd be designing internal tools, connecting platforms with each other and build tools “QA Engineer” is very generic. For example, I often see that software engineers put a lot of their logic in their application layer where as a lot of data engineers put it in the SQL/database layer. An individual who develops software for native usage on computers, mobile devices, televisions, and other devices is known as a software engineer. The high supply has made salaries for DS lower than DE (this is in UK btw). Honestly it'd be tougher to be the former without having done a stint as the latter. Many cloud engineers are senior level because you stumble across huge chunks of code that you have to work with. “SDET” is a dev engineer who generally works on automated test harnesses; it is a coding job, and as such generally demands a higher salary. What you're referring to as a software engineering role is really more in line with data engineering. DS would have more in general, ML engineering may have some if you are customizing models but maybe not too much if you are just training canned models and putting them into production. This is because DS is often seen as a supporting function. ML research has the most but its often PhD level. Additionally, it is easier to prepare for a job as a software engineer than it is for a data scientist. I don't think it's a career ending decision, but I wouldn't recommend it. Across these sectors, data engineers are in high demand -- not necessarily more than software engineers, but certainly up there. However, in smaller companies, the salary difference may be smaller since the job duties may overlap more. Read the sidebar BEFORE posting. In general it's going to depend on the employer and software you work on. Data architect this designation sounds so sexy. Technical Data Engineer (Title: Software Great opportunity. Study IT Consulting or Software Engineering, both at uni. AI is ridiculously overhyped and will not impact either role greatly (ie. ago. Data engineers are still handsomely rewarded by the market right now. I also see each time more DE roles evolving to reach some maturity in terms of software development, whereas much of the evolution come from backend best practices. If you're more interested in the technical aspects of data If I had these options, I would probably take the software engineering course and use the project to do something with some of the DE tools like Spark and a cloud service. Software engineering is a bit more general so there's probably more flexibility. Creates a product that can be used by the other two roles. ). • 7 mo. A data engineer is a software engineer but with specialty in building data intensive software solutions/tools. Those are the data/business analysts, data scientists, and data engineers. Big data engineers are usually slightly higher paid than software engineers. If you ever seen the role "Software Engineer - Machine Learning" that's pretty much interchangeable with ML Engineer. Most of what you're doing as a software engineer is not really tackling "hard problems" in the same way. My 95% of job is writing scripts in python, pyspark and bash to establish automation. Data analyst, its only 10% stats and 90% managing client expectations so harder to be replaced than you would think. And while there can be a lot of overlap in design patterns and standards, after all it's still software engineering, they also use very different tools/frameworks to achieve different goals. Elegant-Road. streaming processing and how streaming actually Put differently, the data engineer builds software to deliver data: a data product. A software engineer will be tasked with building an "app" with X,Y,Z interface that offers U,V,X features with T latency. The entry level candidates to data science positions far exceeds the demand. In accounting - you will start off on a lower salary with similar work life balance but a lot more mobility if you want to go into management. The work is more exciting than data engineering too. Total comp- $100k+ Career path was this: couple years of retail, lucky break getting into tech with a county agency, worked at an isp, then major tech firm as a support engineer, then dc tech at the same company. Most People don't know more than correlation or even how correlation works. For example, in my experience I have noticed that at large non-tech companies and at start-ups data engineers can often take a more software like role because they need to develop a lot of the data infrastructure themselves. I find data engineering more challenging and vast due to it effect on enterpsrise data ecosystem while data science is a module within and non-informed of origin, lineage or effectiveness of data. I would say an average SWE is going to make more than an average AE, but a good AE (top 10% or maybe 20%) is going to make more than a good SWE. sourcing, preparing and providing data sets, dashboards and data models) that’s more of a support role for data science / analytics (and PMs and other functions) and has more limited career growth. But I'd say that go for it! Oftentimes in salary reports or the newer average national salary figures, data engineering is often cited to make more than software engineering, sometimes up to 50k+ more. Short answer is we collect, store, organize, analyze and interpret large data sets. Data engineering itself is a wide spectrum from no code to complex distributed compute and storage systems. I think RPA should be learned by the business themselves and or by business analysts (not software engineers). I've found that SWEs treat data engineers as less skilled/important than software engineers. In addition to common SWE skills, data engineering involves doing stuff that some software engineers can find daunting and hard i. Startups who use data mainly for reporting (e. imo they will converge at some point. The pay between traditional software engineers (aka web developers) and data engineering is pretty similar, and on average data engineers actually make more. Data Engineers are software engineers. Also, system design for software engineers, it seems they are asked to design a larger variety of services. Also, there are more software engineer jobs available in general compared to data science so I presume this plays a role in the amount of job openings between data engineering vs data scientist. Started out as a software engineer, did that for about 2 years until I was put on a project to productionize a machine learning model. It can mean really a lot. I'm getting conflicting info on DE vs SWE salaries. 2. Software is going to provide interesting work + solid work life balance from the start but it's probably going to be a static career after a decade or so (unless you go into management). B2C or B2B SaaS) may hire data teams after bringing on investors that require high quality reporting. Also, a lot of companies require some sort of backend knowledge for DE roles. If I went down the software engineer path I would have a job in Front Line Support. ML Engineer is just a specialized Software Engineer. The Big Data one sounds more like data science to me - it would probably be helpful, but not as generally useful. I would recommend software engineering, assuming it's focused on backend and databases. Hi - before data engineering, was a data scientist for a few roles. A lot are applicable. You can also pivot vertically and assume roles of data architects or the chief data officer for what its worth. It might be because I've been at smaller places with smaller data sets (~20-40 devs/data folks/QA/SRE; <10 TB). If you're more interested in the technical aspects of data IT Consultant vs Software Engineer. Now I’m a cloud engineer that does migrations into AWS. It helps a lot to know how to create software and how to code and best engineering practices. Data roles tend to have extremely fluid titles/roles. If you are not a strong programmer, you’re going to fail miserably as a true cloud engineer. Unless you know in your heart-of-hearts Computer Science / Software Engineering - Min requirement: Projects to Show While computer science and software engineers may stress you getting a degree, it's not required per say. I bet most could probably start out making functions in an API. Go look at linkedin and see how many people apply for DS positions than DE positions. It has more to do with data wrangling and there is heavier focus on data structures and algorithms. I loved working with data, and being able to focus on backend processing and data architecture. Was really good stuff. e. So DE offers more stability long-term. Software engineering knowledge can be quite broad. What job is better data engineer or software engineer/developper based on these question. “Software QA Engineer” is generic but narrows it down to software. in my firm DEs earn more that Software Devs (im Junior DE). A Senior BSWE gets paid $60k less than a Senior SWE. The SWE builds software to deliver a functionality: a software application. WalthamWorks. It is a discipline that, unlike Full Stack Developer Course, largely relies on mathematical and computer Data Engineers, on the other hand, focus more on the architecture and systems that allow for data collection, storage, and accessibility. You get to create more stuff, ship it, and see it affecting The applications of software engineering are diverse and widespread, covering areas such as web and mobile application development, enterprise software solutions, cloud computing, artificial intelligence, and more. to the extent that jobs are 'taken over') in the coming decade. Kubernetes, VPC, docker model productionisation etc. Then there’s the data science side which bridges business, statistics, and tech together. Other engineering roles; Analytics Engineer - responsible for data transformations in the data warehouse. My advice to a colleague with the same question; become a software engineer. Data Science in most company (even in large companies) does not have a clear career path and it rolls up into one of the core functions (engineering, product, marketing) at high levels. The problem with DevOps is that it's a buzz word. Or maybe if there was some kind of machine learning or natural language processing (NLP) accompanied with RPA then it will solve most of the disadvantages I listed. ago • Edited 1 yr. As for salaries, software engineering pays more than data science on average. aimmaz :) The best data engineers were full stack developers early in their career. They are more akin to hvac techs and electricians than typical tech workers Typically, you will be working with Big Data, compiling reports, and sending them to data scientists for study in this capacity. However, data engineering can sometimes be a little more challenging to develop a basic enough project that covers everything that is required for a solid data engineering project. Yes, data engineers should follow swe practices and are a specialized type of SWE. I worked as a software engineer in big tech for 30 years. The big emphasis is the actual design and coding of the software. 1. In practice, DEs usually work with the traditional software engineers as their upstream stakeholders, meaning, SEs build software & systems that generate the internal data that data engineers consume and process. You can do or not do devops in this role. Keep in mind though AE comp is low while you're learning which is not true for SWEs, and the pay is always volatile and inconsistent. Data Scientist vs Data Engineer Salary: According to a review by glassdoor, you may make up to $137,000 per year as a data scientist. Data engineering is closer to revenue for the business imo; it’s related to product. Because an application data model is focused on the activities performed by an application, it also deals with consistency and invariants. Glassdoor says SWE salaries are higher than DE salaries, but anecdotally I've read DE pays more. -Which job has a higher pay? -Which job is more future proof? -Which job has more demand? -Which job has more variety? -Which job has more opportunities to grow? -Which job changes the least? May 1, 2021 · This difference is most common in larger companies where software engineers may earn as much as 40% more than a data engineer. devops needs to work with data more and data engineers, to work with data at scale, needs good devops skills. Current title- Data Center Engineer YOE- 10+ Education- high school diploma. •. @ a startup App worked with the software engineers, and had to do a lot of cloud setup, eg. I recommend you read "Fundamentals of Data Engineering" by Joe Reis. I actually really don't think people who are interested in data science for the ML and statistics will like data engineering that much. However, when you look at large tech companies, data engineers (or "software engineers- data") make either the same, or less than software engineers always, but never more. Becoming a generalist, especially early in the field opens a broad range of opportunities rather than specializing in a single area of study. • 6 days ago. CI/CD, DevOp, git, Agile, are all common place in the DE world now. g. The best part of data science over software engineering is being able to tackle really hard problems. CSCareerQuestions protests in solidarity with the developers who made third party reddit apps. They have fixed modules, and only fixed modules. My theory is that basically every company needs software engineers, including typically low paying industries. Developing Data Engineering Projects On Your Own - One of ways people say to get noticed for software roles is to create a portfolio. the opposite of "clickops", where you sit in front of a computer and click a lot of buttons to install stuff. You will also need to understand the difference between batch vs. EDIT: data scientists usually get paid less than software engineers at the same education level, but there is a wide range and it depends on the technologies you're able to use as well as the solutions you're able to come up with as a DS. Award. Devops is growing too. Data engineer is about data pipelines and processes and big data engineering is about doing it on a big scale with big amounts of data. fi ob qd pf vp xl sf py sz se