Skills to Put on a Resume for a Data Engineer

The skills real Data Engineer postings screen for

Pulled from our Data Engineer role taxonomy — not a generic list. Each one names what a recruiter reads into it and, more usefully, how to actually back it up.

• SQL

  Technical

  SQL is one of the most-requested technical skills on data and analyst job posts — it signals you can pull your own numbers instead of waiting on someone else.

  Evidence, not just a bullet: Show 2-3 real queries you've written — a join, a GROUP BY with aggregation, ideally a subquery or window function — not just “SELECT * FROM table”.

  Practice it:Audit Data Quality for a Climate Tech Sensor NetworkDesigning a Data Warehouse for a Renewable Energy Firm

• Apache Spark

  Signals you can process data at a scale that doesn't fit in a single machine's memory — a different skill from pandas-scale analysis.

  Evidence, not just a bullet: Reference a Spark job you wrote and the dataset size or transformation that actually needed distributed processing.

  Practice it:Migrate a Legacy Warehouse to a Lakehouse for an Edtech AI PlatformScale Feature Pipelines for a Hyperscaler Search-Ranking Team

• Python or JavaScript

  Listing a language only matters if you can point to something it built — recruiters skim past “Python” unless there's a repo or project attached.

  Evidence, not just a bullet: Link a GitHub repo with a script that solves a real problem — data cleaning, an API integration, a small app — with a README that explains what it does.

  Practice it:Audit a Climate-Tech Sensor Dataset for Production ReadinessGPU Cost Dashboard for an AI Consulting Practice

• Airflow DAGs

  Airflow DAGs is one of the specific, verifiable skills recruiters screen for in this role — naming it plainly is what gets it past both an ATS keyword match and a human skim.

  Evidence, not just a bullet: Don't just list "Airflow DAGs" — attach one concrete example of using it: a project, a number, or an artifact a recruiter could actually look at.

  Practice it:Stand Up a Feature Store for a Series-B FintechAutomate Retraining with a Drift-Triggered MLflow Pipeline

• dbt Models

  dbt signals modern-stack fluency — transformation-as-code, testing, and version control for data pipelines, not just ad hoc SQL.

  Evidence, not just a bullet: Reference a dbt model you wrote, including a test you added (unique, not_null, or a custom assertion).

  Practice it:Migrate a Legacy Warehouse to a Lakehouse for an Edtech AI Platform

• AWS or Azure

  Cloud fluency recruiters filter on almost by keyword-match — but “used AWS” and “architected on AWS” read very differently.

  Evidence, not just a bullet: Name the specific services you’ve actually configured — e.g. set up an S3 lifecycle policy, wrote a Lambda, configured an IAM role — not just used passively.

  Practice it:Implement Authenticated Encryption for a Document ServiceContainerize a Legacy Java Monolith and Move It to AWS ECS

• Data Modeling

  Methodology

  Shows you can turn messy inputs into a schema that holds up — the unglamorous skill that keeps a whole analytics stack from breaking.

  Evidence, not just a bullet: Point to an ER diagram or schema you designed, and name the tradeoff you made (normalization vs. query speed, for instance).

  Practice it:GPU Cost Dashboard for an AI Consulting PracticeDesign a Real-Time Order Pipeline for a Fintech Payments Platform

Every challenge below is an AI-generated practice brief — not a real client engagement — that produces a submission you can point to as evidence for the skills above.

• 

  CodeSeniorNew

  Stand Up a Data Platform for a Mobility-Data Startup's First ML Model

  As a 4-person team, build (1) a streaming ingestion path from a simulated telemetry source (Kafka + Python producer is fine); (2) a batch ETL job into a small warehouse (DuckDB …

  • Data Engineering
  • Streaming Ingestion
  • Feature Store

  AI Software Engineering Group Project
• 

  AnalysisBeginnerNew

  Audit a Climate-Tech Sensor Dataset for Production Readiness

  You receive 18 months of raw sensor readings from 1,200 sensors (about 800M rows), plus a sensor-metadata table (location, firmware version, deployment date). Profile the data f…

  • Data Quality Audit
  • Data Profiling
  • Time Series Analysis

  Applied Data Analysis and Practical Data Science
• 

  CodeIntermediateNew

  Build a Serverless ETL Pipeline for a Climate-Tech Sensor Fleet

  Build the pipeline using managed services only (e.g., S3 + Lambda + EventBridge + Glue, or GCS + Cloud Functions + Cloud Scheduler + BigQuery external tables). Source the data f…

  • Serverless Architecture
  • Etl Pipelines
  • Infrastructure As Code

  Cloud Computing for Data and ML
• 

  AnalysisIntermediateNew

  Cost-Optimize a Large-Scale Spark Job for an Ad-Tech Platform

  You receive the Spark job source (PySpark), the EMR cluster config, and 5 nights of job-history JSON. Profile the job with the Spark UI + EMR metrics, identify the top 3 cost dr…

  • Spark Optimization
  • Cloud Services
  • Cost Engineering

  Cloud Computing for Data and ML
• 

  CodeBeginnerNew

  GPU Cost Dashboard for an AI Consulting Practice

  Pull AWS Cost and Usage Report, GCP billing export, and Lambda Labs invoices into a single Parquet table. Implement a tagging convention (project + client + experiment_id) and a…

  • Cloud Cost Attribution
  • Etl Pipelines
  • Data Modeling

  Cloud Computing for Data and ML
• 

  DesignIntermediateNew

  Design a Real-Time Order Pipeline for a Fintech Payments Platform

  You receive a synthetic Kafka stream of around 500 transactions per second, a static merchant dimension table (about 80,000 rows), and a daily FX rate snapshot. Design an end-to…

  • Streaming Data
  • Kafka
  • Stream Processing

  Data Engineering and Big Data Systems