Data Engineer Resume Example & Template (2026)

The U.S. Bureau of Labor Statistics puts the median annual wage for database architects at $144,440 as of May 2025 — the occupational bucket that captures most senior data engineering work. That number sits well above the $122,230 median for software developers broadly, which means companies are actively competing for this skill set. The problem is that the pipeline into that competition starts with an ATS, and most data engineer resumes fail there — not because the candidate lacks the skills, but because the document doesn’t use the right technical vocabulary in the right structural positions.

This page gives you a complete, annotated example resume for a mid-to-senior data engineer, then explains every decision so you can adapt it to your own background.

Complete Data Engineer Resume Sample

Marcus Webb San Francisco, CA · (415) 555-0182 · marcus.webb@email.com · linkedin.com/in/marcuswebb · github.com/mwebb-data

Professional Summary

Data Engineer with 6 years of experience designing and operating large-scale data pipelines on AWS and GCP. Built ELT workflows processing over 4 billion events per day using Apache Spark, Kafka, and dbt, reducing data latency from 6 hours to under 12 minutes. Fluent in Python and SQL; experienced with Airflow for orchestration and Snowflake as the primary cloud data warehouse. Comfortable owning architecture decisions end-to-end and partnering with analytics and ML teams to ship reliable, documented data products.

Experience

Senior Data Engineer Brightline Analytics · San Francisco, CA · Mar 2022 – Present

• Designed and maintained a real-time event ingestion pipeline using Apache Kafka and Spark Streaming that processes 4.2 billion user events per day with 99.97% uptime, replacing a batch job that caused a 6-hour reporting lag for a team of 40 analysts.
• Migrated 18 legacy ETL jobs from a custom Bash/cron setup to Apache Airflow DAGs, cutting pipeline failure rate by 63% over 12 months and reducing on-call incidents from ~14 per month to under 4.
• Built a Snowflake data warehouse modeling layer using dbt, implementing 120+ tested models with full column-level documentation, enabling the analytics team to self-serve 80% of their reporting needs without engineering support.
• Reduced Snowflake compute costs by $47,000 per year by clustering tables on high-cardinality join keys, converting full-table refreshes to incremental dbt models, and right-sizing compute warehouses by workload type.

Data Engineer NovaCrest Financial · Austin, TX · Jul 2019 – Mar 2022

• Built a Python-based ELT framework on AWS (S3, Glue, Redshift) to consolidate data from 12 source systems into a unified customer data platform, reducing time-to-insight for the product team from 3 days to 4 hours.
• Containerized all pipeline jobs using Docker and deployed them on Amazon ECS, eliminating dependency conflicts across three separate engineering teams and cutting deployment time per service from ~90 minutes to under 10.
• Implemented data quality checks using Great Expectations across 35 critical pipeline stages, catching 99.2% of schema drift incidents before they reached production dashboards.
• Partnered with a data scientist to build a feature store for a churn prediction model, engineering 22 time-series features from raw event logs; model went into production with a 3-week lead time, half the original estimate.

Junior Data Engineer Solaris Digital · Remote · Jun 2018 – Jul 2019

• Wrote Python scripts to automate daily ingestion of 15 third-party API feeds into a PostgreSQL data warehouse, replacing a manual analyst workflow that consumed ~8 hours per week.
• Maintained and documented SQL transformation logic for a legacy reporting pipeline, improving query performance by 40% through index optimization and query refactoring.

Skills

Languages: Python (pandas, PySpark, SQLAlchemy), SQL, Scala (working knowledge), Bash
Orchestration & Streaming: Apache Airflow, Apache Kafka, Apache Spark, Spark Streaming
Data Warehouses & Databases: Snowflake, Amazon Redshift, BigQuery, PostgreSQL, MySQL
Transformation: dbt (data build tool), Apache Spark SQL
Cloud Platforms: AWS (S3, Glue, Redshift, EMR, ECS, Lambda), GCP (BigQuery, Dataflow, Cloud Composer)
Infrastructure & DevOps: Docker, Kubernetes, Terraform, GitHub Actions, CI/CD
Data Quality & Governance: Great Expectations, dbt tests, Apache Atlas
Other: Data Modeling (Kimball, Data Vault), REST APIs, Apache Hadoop (HDFS, Hive), Looker

Education

B.S. Computer Science University of Texas at Austin · 2018
Relevant coursework: Distributed Systems, Database Systems, Algorithms, Cloud Computing

Why This Resume Works: Section-by-Section

The Summary

The summary above earns its place by doing three things most summaries skip. First, it leads with a concrete scale signal — “4 billion events per day” — that immediately orients a hiring manager to the candidate’s operating environment. Second, it names the core stack explicitly (Spark, Kafka, dbt, Airflow, Snowflake) rather than gesturing at “big data tools,” because ATS systems parse the summary just like any other section. Third, it closes with a collaboration note (“partnering with analytics and ML teams”) that signals the cross-functional communication skills that distinguish senior engineers from those who only write code.

What to change for your version: replace the stack names with the stack named in the job description you’re targeting, and replace the scale figure with your own. If you don’t have a daily-events number, use data volume (TB processed per run), team size impacted, or pipeline count. Avoid vague phrases like “passionate about data” — they consume space that should hold measurable context.

Experience Bullets

Every bullet in this example follows the same architecture: action verb + technical context + numeric outcome. That structure is not cosmetic — it maps to exactly what a recruiter and a hiring manager each need. The recruiter needs to clear the ATS and spend 15 seconds confirming you’ve done the job before. The hiring manager needs to know whether the scale you’ve operated at matches their environment.

Notice the specificity of the numbers:

• “63% reduction in pipeline failure rate” beats “improved reliability significantly”
• “$47,000 annual cost reduction” is more memorable than “optimized Snowflake costs”
• “4.2 billion events per day with 99.97% uptime” sets a concrete operating bar

If you don’t yet have exact numbers, use estimates: “approximately 500 GB per day,” “reduced time from ~3 days to 4 hours.” Approximations still signal rigor. What recruiters distrust are adjectives — “large,” “significant,” “improved” — that carry no reference point.

Verb choices matter. Use verbs that imply ownership: designed, built, migrated, implemented, engineered. Verbs like “assisted with” or “helped support” are accurate for junior contributions but they signal a narrower scope of responsibility than employers want at the senior level.

Skills Section

The skills section in this example is structured by category rather than as a flat alphabetical list. This matters for ATS for one non-obvious reason: some systems extract skills by proximity to category labels, and a flat list can cause a parser to lose context. More practically for a human reader, grouping by category lets a hiring manager scan in under 10 seconds for the tool they care about most.

The order of categories follows the typical JD priority for a data engineering role: languages first (since Python and SQL are mentioned in nearly every posting), then orchestration/streaming (Airflow and Kafka are the two most common discriminating requirements in 2026 JDs), then warehouses and transformation.

One decision to flag: the example includes Scala as “working knowledge” in parentheses. Be honest about your proficiency level in exactly this way — it is far better to signal a partial skill than to omit it entirely (Scala appears in a growing share of Spark-heavy JDs) or to claim full proficiency when you’d struggle in a technical screen.

Education

At mid-to-senior level, education is a one-liner. It belongs at the bottom. GPA, honors, and extracurriculars are worth including only if you graduated within the last 3 years. The coursework line above serves one purpose: it seeds terms like “Distributed Systems” and “Database Systems” that appear in some JDs and ATS keyword lists, without taking up meaningful space.

ATS Keyword Guidance for Data Engineer Roles

The following terms appear across the majority of data engineering job descriptions posted in 2026. The left column is what you should write; the right column shows the common mistake.

Three additional keyword clusters that show up in nearly every mid-to-senior data engineer JD and are routinely missing from resumes:

Data quality and observability. Terms like “data quality,” “data validation,” “Great Expectations,” “Monte Carlo,” and “data observability” are increasingly required rather than optional. If you have experience building data quality checks — even simple ones — surface it explicitly.

Orchestration specifics. “DAGs,” “pipeline orchestration,” and “workflow automation” are searched for separately from “Airflow” by some parsers. Use all three in context across your bullets and skills section.

Cloud-specific service names. “AWS Glue,” “Amazon EMR,” “GCP Dataflow,” “Cloud Composer,” and “Azure Data Factory” are evaluated as distinct terms from their parent cloud. If you’ve used one of these services, name it by its full product name at least once in the document.

Where to place keywords for maximum ATS weight: Title/headline > Summary > First bullet of most recent job > Skills section. Burying a critical term like “Apache Kafka” only in a skills section gives it less weight than placing it in a bullet at the top of your most recent role. Wherever space allows, mirror the job description’s exact phrasing in your most recent experience entry.

5 Common Data Engineer Resume Mistakes

1. Listing tools without scale or context

“Proficient in Apache Spark, Kafka, Airflow” tells a recruiter nothing about the size of the problems you’ve solved. A candidate who wrote a weekend Kafka demo and a candidate who ran a 10 TB/day production pipeline look identical with this phrasing. Every tool mention in your experience section should be anchored to volume, frequency, or impact. Even a number like “3 Airflow DAGs managing 8 upstream dependencies” is more credible than an unanchored claim.

2. Writing “ETL” when you mean “ELT”

These are not synonyms, and hiring managers notice the distinction. ETL (Extract, Transform, Load) is the traditional approach where transformation happens before loading. ELT (Extract, Load, Transform) is what most modern cloud-native stacks — Snowflake + dbt, BigQuery + dbt, Redshift + dbt — actually do. Using the wrong term for your stack signals either imprecise writing or genuine unfamiliarity with the tools. Match the term to the architecture you actually built.

3. Omitting data modeling from the skills section

Data modeling — specifically Kimball star schema, Data Vault, or OBT (One Big Table) for analytics engineering — appears explicitly in a large share of 2026 data engineering JDs. It is not assumed from the presence of “SQL” or “dbt.” If you’ve designed dimensional models, fact/dimension table structures, or Data Vault hubs and satellites, say so in those words.

4. Using a one-size-fits-all skills list

Data engineering roles span a huge spectrum: streaming-heavy (Kafka, Flink), batch-heavy (Spark, Hadoop), analytics-engineering-focused (dbt, Snowflake), or platform/infrastructure-heavy (Kubernetes, Terraform). A resume with every tool in the ecosystem looks unfocused and triggers skepticism in technical interviewers. Tailor your skills section per application: lead with the three to five tools the JD emphasizes most, and move less relevant tools down or into a secondary cluster.

5. No mention of data quality or documentation

Modern data engineering is not just building pipelines — it’s making data trustworthy and usable by downstream consumers. Hiring managers at data-mature companies actively look for evidence that you’ve implemented data quality checks (Great Expectations, dbt tests, custom assertions), maintained data catalogs or lineage documentation, or established SLAs for pipeline freshness. A resume with no evidence of these practices signals a candidate who hands off broken data and considers their job done. Even one bullet that mentions testing, validation, or documentation signals the maturity they want.

If you want to build a version of this resume tailored to a specific job description — with your own experience, auto-formatted bullet structure, and ATS keyword match score — OfferFlow’s resume builder lets you start from scratch or import an existing document and iterate from there.