A template for data engineers who build the pipelines every analyst and AI model quietly depends on.
Data engineers build and run the pipelines that move, clean, and serve data at scale. They design ingestion, write ETL and ELT jobs in tools like Airflow and dbt, model warehouses in Snowflake or BigQuery, and keep streaming and batch flows reliable when traffic spikes at 2am. They sit upstream of analysts, data scientists, and increasingly AI teams, who all depend on clean, timely data. A normal week mixes pipeline development, schema design, performance tuning, and the occasional incident response. The role rewards strong engineering, real SQL and Python fluency, and a habit of building for the day a job fails rather than the day it works in the demo. Recruiters read this CV looking for stack, scale, and proof you keep data trustworthy.
Data Engineer with 6 years building cloud data platforms in fintech and retail. Designed dbt and Airflow pipelines processing 2TB per day at 99.9% on-time delivery, and cut warehouse compute cost by a third through partitioning and model redesign. Fluent in Python, SQL, Spark, and Snowflake, and comfortable owning reliability end to end.
Stack and scale, in that order. A line like "Built dbt and Airflow pipelines processing 2TB per day at 99.9% on-time delivery" tells a hiring manager far more than "worked with data." They want to see the warehouse you used, the volume you handled, and proof you kept it reliable. Naming Snowflake, BigQuery, or Redshift with a real workload behind it beats a long tool list every time.
Python and SQL are the floor, not the ceiling. Add Spark for big data, Airflow for orchestration, dbt for transformation, Kafka for streaming, and a cloud warehouse like Snowflake or BigQuery. Round it out with data modeling and a cloud platform such as AWS, GCP, or Azure. Recruiters cross-check these against the job description, so list the ones you genuinely shipped with.
The big one is listing tools with no pipelines attached. A wall of logos tells nobody what you built or how reliable it was. The other trap is vague impact: "improved performance" means nothing without the cut in run time or cost. Show scale and reliability, and skip fabricated precision you can't defend in an interview.
Keep it to one or two pages. Lead with a short summary that names your core stack, then your experience newest first. A clean projects or achievements line works well: pipeline, scale, stack, and impact in one sentence. Use plain section headings, a single accent color, and a readable font like Inter at 11pt so an ATS reads it without choking.
Figures in USD. Ranges reflect mid-level experience (3–7 years). Senior roles and major metro areas typically sit at the top of these bands.
Tech firms, banks, and large enterprises want Python, SQL, Spark, Airflow, dbt, and a cloud warehouse such as Snowflake, BigQuery, or Redshift named alongside the real pipelines you ran on them. A dbt certification or an AWS or Azure data credential helps you clear filters. Put your stack, your pipeline scale, and a reliability win near the top, because that's the line a hiring manager scans for first.
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