Data Engineer resume example

Data engineering resumes get judged on whether pipelines run reliably at scale, so quantify data volume, pipeline runtime, and failure rate the same way a backend engineer quantifies uptime. Name the orchestration tool and warehouse explicitly since that's the first ATS keyword match.

Show ownership of the full pipeline lifecycle — ingestion, transformation, and the downstream table or dashboard it feeds — rather than describing isolated ETL scripts with no context for who used the output.

Farrukh Islamov

Data Engineer

Phoenix, AZ farrukh.islamov@email.com +1 (602) 555-0129 linkedin.com/in/farrukhislamov github.com/fislamov-data

Summary

Data engineer with 6 years building high-volume ingestion and transformation pipelines on Spark, Airflow and Snowflake. Cut pipeline failure rate from 6% to 0.4% while scaling daily ingestion to 400M events across a logistics data platform.

Experience

Senior Data Engineer

Cascadia Data Works · Phoenix, AZ

Feb 2022 — Present

Built an Airflow pipeline ingesting 400M events/day into Snowflake, cutting pipeline failure rate from 6% to 0.4% via dbt-based data quality tests.
Migrated batch ETL jobs to Spark structured streaming, reducing data latency for downstream dashboards from 4 hours to 12 minutes.
Partitioned and converted 8 core tables to Parquet on S3, cutting monthly warehouse compute costs 34%.

Data Engineer

Northmere Freight · Phoenix, AZ

Sep 2018 — Jan 2022

Built a Kafka-based CDC pipeline replicating 15 production tables into Redshift, replacing a nightly batch job that took 6 hours.
Wrote dbt models and tests standardizing shipment data across 3 regional systems, cutting reporting discrepancies to near zero.
Automated schema-change detection with Terraform-managed infrastructure, reducing pipeline breakage incidents by 60%.

Skills

Apache Spark · Airflow · Kafka · dbt · Snowflake · Python · SQL · AWS Redshift · S3 · Terraform

Education

Arizona State University

B.S. Computer Science

2014 — 2018

Data Engineer · Engineering Minimal template · single-column, ATS-safe.

ATS keywords for a Data Engineer resume

Applicant tracking systems match your resume against the job description's vocabulary. Mirror the terms below that are true of you — ideally with a metric attached.

Apache SparkAirflowKafkadbtSnowflakePythonSQLAWS RedshiftS3ETL/ELTData WarehousingParquetCI/CDTerraform

What recruiters look for

The orchestration tool (Airflow, dbt) and warehouse (Snowflake, Redshift, BigQuery) named explicitly.
Data volume or pipeline scale stated — records/day, terabytes processed, table count.
Pipeline reliability metrics: failure rate, SLA adherence, runtime reduction after optimization.
Evidence the engineer partnered with analysts or data scientists on schema design, not just plumbing.
Data quality or testing practices (dbt tests, Great Expectations) showing pipelines are trustworthy, not just fast.

Before & after: one bullet

Weak

Built ETL pipelines to move data into the data warehouse.

Strong

Built an Airflow pipeline ingesting 400M events/day into Snowflake, cutting pipeline failure rate from 6% to 0.4% via dbt-based data quality tests.

Common mistakes to avoid

Describing pipelines built with no data-volume or runtime number attached.
Confusing data engineering with data analysis — reporting output instead of infrastructure built.
No mention of data quality checks, leaving reviewers unsure if downstream tables are trustworthy.
Omitting the cloud data warehouse entirely when it's a primary ATS filter term.