Data Engineers Job Trends after Gen AI

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-**Data Engineer Salaries:**Average base salary is $125,000, with experienced professionals earning $150k+ and additional compensation through bonuses and equity. -Explosive Job Growth:~50% year-over-year growth in job market demand, making it one of the fastest-growing tech occupations. -**Skills in High Demand:**Core requirements include SQL, Python/Scala/Java, big data frameworks (Spark, Hadoop), and cloud platforms. -**Talent Shortage:**Companies are creating positions faster than qualified candidates are entering the field, leading to competitive compensation packages. -**Remote Work:**Data engineering roles are highly compatible with remote work.

Data Engineers

Salary Trends

-**High Averages:**Data engineers earn salaries on par with software engineers. Theaverage base salary for a Data Engineer is about $125,000 per year in the U.S.builtin.com. Median salaries hover around $120k, with additional bonuses often pushing total pay near $150kbuiltin.com. This places data engineers among the better-paid IT professionals, reflecting their specialized skills. -**Range by Experience:**Entry-level data engineers (with ~0-2 years experience) might see offers in the$90k – $110krange. Mid-level data engineers (~3-5 years) commonly earn between$118k and $150kmotionrecruitment.com. Senior data engineers (7+ years experience) often earn well into the$150k+ rangeand can approach $170k or more at top companieslinkedin.com. In tech hubs, a senior data engineer’s base salary can average ~$160k (e.g., San Francisco)linkedin.com. -**Steady Upward Trend:**Data engineer salaries have shownsteady growthover the past few years. For example, theaverage rose from about $100k in 2018 to $124k by 2023linkedin.com. This ~24% increase in five years highlights strong demand. In 2025, salaries continue to rise moderately as organizations invest in data infrastructure. While not skyrocketing like early big data days, pay is still increasing a few percent annually. -**Competitive Offers:**Companies often need to offer very competitive pay to attract experienced data engineers, since these professionals are in short supply. It’s not unusual for a seasoned data engineer to receive multiple offers. As a result, many employers include perks like sign-on bonuses, stock options, or remote work options in addition to salary to sweeten the deal. -**Industry Variations:**Data engineer pay can vary by industry and company size. Tech firms and financial services typically pay the most (often 6-figure base salaries even for relatively junior roles). Smaller companies or those in industries like education may pay somewhat less. However, because data engineering skills are so transferable, there’s a tendency towardmarket-rate leveling – talented data engineers often move to higher-paying sectors, pushing even traditionally lower-paying industries to raise salaries to attract talent. -**Total Compensation:**Besides base salary, many data engineers, especially in large companies, get bonuses (~10-15% of base) and equity. Therefore, an average total compensation might be ~$150k (e.g., average base $125k + bonus). Top performers could have total comp in the $180k-$200k range. Contract data engineers (consultants) might bill at high rates that annualize well above these figures, indicating companies are willing to pay a premium for short-term access to these skills as well.

Data Engineer Salary Highlights:

Level	Typical Base Salary
Entry-Level	~$95k – $110k (junior DE)
Mid-Level	$120k – $130k (3-5 years exp)
Senior	$140k – $170k (highly experienced)
Top Markets (Sr.)	~$160k+ (e.g. SF, NYC average)linkedin.com
Average (All Levels)	$125,000(base)builtin.com
Median	~$120,000(base)builtin.com

Job Growth Projections

-**Booming Demand:Data engineering is often cited as one of thefastest-growing tech occupations. The demand for data engineers has been growing at an explosive rate – some reports indicate~50% year-over-year growth in the job market for data engineersin recent yearslinkedin.comlinkedin.com. This is an extraordinary growth rate, reflecting how rapidly companies are building out their data infrastructure. -“Fastest Growing Job” Tag:**By various analyses,data engineer ranks among the top emerging jobs. It was noted as the fastest-growing role in tech with ~50% growth each year, and was ranked 6th among the top 50 most sought-after roles in one 2020 surveylinkedin.comlinkedin.com. Even if growth moderates, the trajectory is sharply upward. -**Drivers of Growth:**The surge is driven by theexplosion of data. Organizations are collecting more data than ever and need professionals tobuild data pipelines, warehouses, and lakes. The rise of AI and analytics in business means raw data must be refined – data engineers are the ones who architect and manage those pipelines. Every industry from tech to retail to healthcare is investing in data engineering to enable data science and business intelligence. -**Shortage of Talent:**Job postings for data engineers often outnumber available qualified candidates, which is why growth in open positions is so high. Essentially,companies are creating data engineering jobs faster than people are entering the field, leading to a talent crunch. This is expected to continue for the next few years. -**Outlook:**The outlook for data engineers is exceptional. As long as “data is the new oil,” those who can handle big data will be in demand. Some projections align data engineers with the broader “data scientists” category growth (which BLS pegs at 36% for 2023–2033) – indeed data engineers are part of that data ecosystem boom. We anticipatestrong double-digit growth annuallyin the near term, tempering to high single-digit growth by the late 2020s as the field matures. Even then, it will likely remain one of the hotter tech careers. -**Geographical Spread:**Initially, data engineer jobs were concentrated in tech hubs, but 2025 sees widespread demand across regions. Many mid-size cities and non-tech companies are now hiring their first data engineers. This geographic spread also contributes to growth – new employers (who never had these roles before) are now adding them. -In Summary:Thedata engineer job market in 2025 is “booming”, with opportunities plentiful. Anyone with the right skills is unlikely to stay on the job market for long, and many companies are expanding their data teams significantly each year.

Data Engineer Job Market Indicators:

Indicator	Trend (2025)
Demand Growth	~50% YoYin recent yearslinkedin.com (exceptionally high)
Status	Fastest-growing tech job (labeled in multiple surveys)linkedin.com. Talent shortage ongoing.
Projected Outlook	Continued high growth next 5+ years as data volumes and AI initiatives expand. Slowing slightly as supply catches up.
Hiring Difficulty	Very high – roles often open for long due to lack of qualified applicants. Companies competing on salary & perks.

In-Demand Skills

-**SQL and Database Expertise:**At the core of data engineering isstrong SQL skills and database knowledge. Virtually every job listing requires proficiency in SQL for querying and managing data. Data engineers need to design and optimize relational databases, and increasingly work with NoSQL and NewSQL databases too. Knowledge of data modeling (star schemas, normalization) is fundamental. -**Programming (Python/Scala/Java):**Data engineers are basically software engineers focused on data pipelines.Pythonis extremely common (used for ETL scripts, data manipulation, etc.), as isScala or Javain big data ecosystems (e.g., Spark jobs often written in Scala/Java). Mastery in one or more of these languages is a must. Python’s easy syntax and rich ecosystem (Pandas, Airflow, etc.) make it a go-to tool, whereas Scala/Java give performance on large-scale data processing. -**Big Data Frameworks:**Skills inbig data processing frameworks like Apache Spark and Hadoopare highly sought. Many job descriptions explicitly mention Spark (for batch and streaming processing) and sometimes MapReduce/Hive for Hadoop-based stacks. Kafka (for data streaming) is another big one – knowing how to build data pipelines with Kafka for real-time data ingestion is a valuable skilllinkedin.comlinkedin.com. -**ETL/ELT and Data Pipelines:**Data engineers should know how to design and implementETL/ELT pipelines– extracting data from sources, transforming it, and loading into data warehouses or lakes. Experience with workflow orchestration tools likeApache Airflow, AWS Glue, dbt(data build tool) is a plus. Many companies want data engineers who can automate pipeline workflows and handle dependencies, scheduling, and error recovery. -**Cloud Data Services:**With data infrastructure moving to the cloud, familiarity withcloud data servicesis often required. For example:AWS Redshift, S3, EMR, Glue; Azure Synapse, Azure Data Factory; Google BigQuery, Dataflow, Dataproc. Being able to build data pipelines using cloud-native tools is a common job requirement. Certifications like AWS Certified Data Analytics or Google Professional Data Engineer can be indicative of these skills. -**Data Warehousing:**Knowledge ofdata warehousing conceptsand platforms (Snowflake, Amazon Redshift, Google BigQuery, etc.) is important. Data engineers often set up and manage warehouses where cleaned data is stored for analysis. Understanding how to optimize storage (partitioning, clustering) and query performance in these systems is valuable. -**NoSQL and Data Lakes:**Beyond relational data, data engineers work withunstructured or semi-structured data. Skills in NoSQL databases (MongoDB, Cassandra) and buildingdata lakes(e.g., on S3/HDFS with formats like Parquet/Avro) are in demand. This allows handling of diverse data types at scale. -**Data APIs and Streaming:**Buildingdata pipelines in real-timeis a growing area. Skills in streaming technologies (Kafka, Kinesis) and being able to create APIs for data access are often requested. For instance, a data engineer might need to pipeline event data from a Kafka topic into a database in real-time. -**DataOps and Tooling:**Data engineers increasingly adopt DataOps practices – similar to DevOps but for data. Knowledge of version control for data pipelines, CI/CD for data workflows, and infrastructure-as-code (Terraform, CloudFormation for data infrastructure) is emerging as a desirable skill setmatillion.com. Automation and reproducibility in data pipelines is a theme in advanced roles. -**Problem Solving & Big Picture:**Aside from tools, a good data engineer has strong problem-solving skills to deal with messy data, optimize pipeline performance, and ensure data reliability. They need to understand thebig picture of data flow in an organization– from source systems to analytical consumers – so they can build robust systems. -**Collaboration with Data Scientists:**Data engineers often work closely with data scientists and analysts. So, understanding the needs of data science (e.g., providing cleaned data sets, feature tables, or implementing pipelines for machine learning models) is valuable. Familiarity with machine learning concepts or at least how data feeds into ML can be a plus, though not as central as the engineering aspects.

Top In-Demand Skills – Data Engineers:

Skill	Description / Tools
SQL & Database Design	Writing complex SQL, designing schemas; working with relational DBs (MySQL, PostgreSQL) and NoSQL stores. Core skill for managing structured data.
Python/Scala/Java Programming	Coding data processing jobs and pipelines. Python (with libraries like Pandas, PySpark) for ETL; Scala/Java for Spark jobs or enterprise ETL systems.
Big Data Frameworks (Spark, Kafka)	Using Apache Spark for large-scale data processing (batch & streaming)linkedin.com; Kafka for real-time data streaming. These enable handling Big Data volumes.
ETL & Pipeline Orchestration	Building ETL pipelines with tools like Airflow, AWS Glue, or dbt. Scheduling, error handling, and maintaining data workflows.
Cloud Data Platforms	AWS/GCP/Azure data services (S3, Redshift, BigQuery, etc.). Deploying and managing data infrastructure in the cloud.
Data Warehousing	Knowledge of warehouse solutions (Snowflake, Redshift) and techniques for optimizing analytic queries.
Data Lakes & NoSQL	Managing data lakes for unstructured data; using NoSQL databases for scalable storage. Ensures flexibility in handling different data types.
DataOps & DevOps	Automation skills – using CI/CD for data pipeline code, infrastructure as code, and overall data workflow management for reliability and reproducibility.

Remote Work Trends

-**Remote-Friendly Role:**Data engineering work is highly digital and server-based, which makes it quite amenable toremote work. Many data engineers work remotely in 2025, similar to their software developer counterparts. As long as they have access to databases and cloud platforms via the internet, they can build and monitor pipelines from anywhere. -**Hybrid Teams:**Companies often have distributed data teams. It’s common for data engineers to collaborate remotely with data scientists or with platform engineers. Thus,hybrid and fully remote arrangements are common. Organizations have adapted by using collaboration tools, and by scheduling periodic in-person meetings if needed. -**Security and Access:**One consideration is data security – some companies with very sensitive data infrastructure prefer data engineers to be on-site or on secure networks. However, solutions like VPNs and cloud security controls usually mitigate this, allowing remote work without compromising security. -**Remote Percentage:**While exact figures are hard to pin down, data roles (including engineers and scientists) have a high remote work incidence. In fact, one report noteddata analysts had slightly lower remote rates than data scientists365datascience.com – implying data scientists and by extension data engineers see significant remote work adoption. It’s safe to say a large portion (likely 40-50% or more) of data engineering jobs offer remote/hybrid options in 2025. -**Global Collaboration:**Data engineering often involves working with global data (and sometimes global teams). Remote work allows companies to hire data engineers in different regions to ensure coverage and tap into a wider talent pool. For example, a company might have one data engineering team member in New York, another in Bangalore, working together. This is facilitated by remote work norms. -**Challenges and Adaptations:**The challenges in remote data engineering are similar to those in other remote software roles – ensuring clear communication, managing tasks across time zones, and maintaining system uptime. Many data teams use agile methodologies with virtual stand-ups. Monitoring pipelines remotely has also improved with sophisticated cloud monitoring tools. -**Flexibility as a Perk:**Employers often tout remote work as a perk to attract data engineers, given the talent shortage. Conversely, many skilled data engineers now expect or negotiate for remote flexibility. This has solidified remote work as a standard aspect of the data engineer role rather than an exception. -**Conclusion:**Remote work is well-entrenched for data engineers.Outside of specific cases requiring physical presence, data engineers enjoy the freedom to work from home or anywhere with internet. Companies focus on results (data pipeline reliability, data delivery) rather than location. We can expect this trend to continue, with perhaps occasional office meet-ups for team building or strategic planning.

Hiring Challenges

-**Severe Talent Shortage:**Perhaps more than any role in this report,finding experienced data engineers is a major challenge. Demand has far outstripped supply in recent years. Many companies are looking for their “first” data engineer to build infrastructure, but there are simply not enough seasoned data engineers actively job-seeking to fill all the openings. This leads to positions staying vacant for long periods and intense competition when a strong candidate appears. -**Experience vs. Education:**A lot of professionals have started adding data engineering skills (e.g., taking bootcamps or online courses), buttrue hands-on experience is hard to come by. Building production data pipelines and solving real-world data problems is different from academic exercises. Employers often list a requirement of several years of experience with specific big data tools, which narrows the field. The result is that many applicants are relatively junior, while the roles demand senior-level expertise, creating a mismatch. -**Evolving Tech Stack:**The data engineering tech stack is evolving rapidly (Spark replacing MapReduce, cloud replacing on-prem, streaming data rising, etc.).Keeping up with the latest toolsis a challenge for engineers and hiring managers alike. Companies may seek candidates with very specific tool experience (say, Kafka + Spark streaming + Snowflake). Such specific combos can be hard to find, leading employers to either compromise on requirements or invest in training after hiring. For candidates, demonstrating adaptability and quick learning is key, but hiring processes don’t always account for potential vs. exact skill match. -**High Salary Expectations:**The shortage means that many capable data engineers can command high salaries or multiple offers. This can be a hurdle for startups or smaller firms that can’t match big tech salary offers. Hiring managers often face scenarios where their top choice candidate is scooped by a tech giant or asks for a compensation that’s above budget.Salary and benefits negotiations are toughwhen candidates know their market value is high. -**Retention and Turnover:**Even after hiring, retaining data engineers is challenging. They are often poached or lured by new opportunities (perhaps to work on cutting-edge tech or get a title bump to “Senior Data Engineer” or “Data Architect”). High turnover can plague data teams, which in turn makes hiring an ongoing effort. Employers are responding by creating clearer career paths for data engineers (e.g., progression to lead data engineer or management) to improve retention. -**Role Clarity:**In some organizations, thedata engineer role is not well understoodoutside the tech team. This can lead to mis-hiring or frustration – e.g., hiring a “data engineer” when what the company actually needed was a data analyst or a database administrator. Conversely, some data engineers find themselves doing a lot of data analytics if the company lacks other data roles. This lack of role clarity can make it tricky to write accurate job descriptions and find candidates who fit the company’s specific needs. Savvy candidates will probe what exactly the job entails; misalignment can cause offers to be declined or new hires to feel unhappy in the role. -**Competition with Data Science Roles:**There has been a trend of more people training as data scientists (because it’s been hyped as the “sexiest job”), but fewer focusing on data engineering. Ironically, many companies have realized they first need data engineers to make data scientist work possible. This meanscompanies are trying to convert or retrain some talent into data engineering, but not everyone is interested in that switch. Hiring managers sometimes attempt to target software engineers and entice them into data engineering. There’s a bit of a marketing challenge – making the role attractive (it’s sometimes seen as more “behind-the-scenes” compared to the flashy AI work of data scientists). -**Upskilling and Internal Promotion:**Given hiring difficulties, some firms invest inupskilling developers or ETL specialists into data engineer roles. This can fill gaps but takes time. In the interim, the burden on existing data engineers is high – they are often overworked, which can lead to burnout or departures, exacerbating the hiring problem. It’s a vicious cycle: too few data engineers means the ones in place have too much to do, which can drive them away, making hiring even more critical. -**Summary:**Hiring data engineers in 2025 is asignificant challenge marked by scarcity of experienced talent and competitive labor market conditions. Companies are finding creative ways to cope: offering remote work to widen candidate search, increasing salaries, considering candidates from adjacent disciplines, and building talent pipelines through internships and training programs. Nonetheless, expect the hiring crunch for data engineers to persist in the near future, until the talent supply catches up with the booming demand.

About the Author

Rick Hightower is a technology analyst and industry expert specializing in data engineering trends and workforce development. With extensive experience tracking technological shifts and their impact on job markets, Rick provides strategic insights to help professionals navigate career transitions in the rapidly evolving tech landscape.

Connect with Rick on LinkedIn: https://www.linkedin.com/in/rickhigh/

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