Resume Keywords for Data Engineers

Data engineer job descriptions are more tool-specific than almost any other role. Where a marketing manager JD might list "data-driven decision making" as a soft requirement, a data engineer JD will specify "Airflow 2.x," "dbt Core," or "Delta Lake." ATS systems are calibrated to match on these exact strings.

The most common filtering mistakes for data engineers:
- Listing "pipeline development" without naming the orchestration tool (Airflow, Prefect, Dagster, Mage)
- Writing "cloud experience" without specifying the platform and its specific services (AWS Glue vs. Google Dataflow vs. Azure Data Factory are not interchangeable)
- Using generic terms like "big data" instead of the specific compute engine (Spark, Flink, Presto/Trino)
- Missing the storage layer (Delta Lake, Iceberg, Hudi) — increasingly required in 2026 JDs

Use the resume keywords checker to identify the exact tool names a specific JD requires that your resume is missing.

Orchestration tools:
- Apache Airflow, Airflow 2.x, DAGs (Directed Acyclic Graphs)
- Prefect, Dagster, Mage, Luigi
- dbt (data build tool), dbt Core, dbt Cloud, dbt models, dbt tests

Processing engines:
- Apache Spark, PySpark, Spark SQL, Spark Streaming, Structured Streaming
- Apache Flink (for streaming roles)
- Apache Kafka, Kafka Streams, Kafka Connect, event streaming
- Presto, Trino (for query-heavy roles)

Pipeline concepts:
- ETL (Extract, Transform, Load), ELT
- Batch processing, stream processing, real-time data pipelines
- Data ingestion, data integration, Change Data Capture (CDC)
- Pipeline monitoring, SLA, data freshness
- Medallion architecture (Bronze / Silver / Gold layers)

ATS filters are highly specific to cloud platform. A JD that says "BigQuery" does not match "Redshift" — include the exact tools for the platform in the job description.

AWS data stack:
- Amazon S3, AWS Glue, AWS Glue ETL, Amazon Redshift, Amazon EMR
- AWS Lambda (for event-driven pipelines), Amazon Kinesis, AWS Step Functions
- AWS Athena, Lake Formation, AWS Databricks

Google Cloud (GCP) data stack:
- BigQuery, Google Cloud Storage (GCS), Dataflow (Apache Beam)
- Pub/Sub, Dataproc, Cloud Composer (managed Airflow), Looker
- dbt + BigQuery integration

Azure data stack:
- Azure Data Factory (ADF), Azure Synapse Analytics, Azure Blob Storage
- Azure Databricks, Azure Event Hubs, Azure Stream Analytics, Azure Data Lake Storage (ADLS)
- Microsoft Fabric (increasingly mentioned in 2026 JDs)

Cloud-agnostic / multi-cloud:
- Snowflake, Databricks, Delta Lake, Apache Iceberg, Apache Hudi
- Fivetran, Airbyte, Stitch (ingestion tools)
- Terraform, Pulumi (infrastructure as code for data platforms)

This category separates mid-level from senior data engineer resumes. As data platforms have matured, JDs now explicitly require quality and governance experience.

Data quality:
- Great Expectations, Soda, dbt tests, data quality checks
- Data validation, schema validation, data contracts
- Observability: Monte Carlo, Atlan, Bigeye

Data governance:
- Data lineage, data cataloguing: Amundsen, DataHub, Alation, Collibra
- Unity Catalog (Databricks), BigQuery Data Catalog
- PII detection, data masking, GDPR compliance, data access controls
- Role-based access control (RBAC) for data

Architecture patterns:
- Data lakehouse, Lambda architecture, Kappa architecture
- Data mesh, data domain ownership
- Star schema, snowflake schema, dimensional modelling
- Slowly Changing Dimensions (SCD Type 1, 2, 3)

Performance:
- Query optimisation, partition pruning, Z-ordering, clustering
- Cost optimisation (cloud spend per TB, compute efficiency)
- Data compaction, vacuuming (Delta Lake / Iceberg)

Languages:
- Python (with libraries: pandas, PySpark, SQLAlchemy, boto3, google-cloud)
- SQL — specify variants: BigQuery SQL, Snowflake SQL, Spark SQL, PostgreSQL
- Scala (for Spark-heavy roles at large tech companies)
- Bash / shell scripting (for pipeline automation)
- YAML (for Airflow DAG configs, dbt project files)

Infrastructure and DevOps:
- Docker, Kubernetes, Helm (for containerised data platforms)
- Terraform, CloudFormation (infrastructure as code)
- CI/CD: GitHub Actions, GitLab CI, Jenkins (for data pipeline deployments)
- Git, version control, pull request workflows

Monitoring and alerting:
- Datadog, Grafana, PagerDuty (for pipeline monitoring)
- Logging: CloudWatch, Google Cloud Logging, Azure Monitor

Every keyword should appear in context. The skills section alone is not enough — ATS systems and hiring managers both weight keywords that appear in achievement bullets.

Structure: [Action verb] + [specific tool/technology] + [scale or business impact]

Examples:
- "Built end-to-end ELT pipeline using dbt Core and Snowflake processing 800M+ daily events, reducing reporting latency from 6 hours to 15 minutes"
- "Migrated legacy batch Spark jobs to Databricks Delta Live Tables, cutting infrastructure costs by 40% and eliminating 3 hours of daily manual monitoring"
- "Designed Airflow DAG framework with modular task groups for 120+ pipelines, enabling the data team to onboard new sources 3x faster"
- "Implemented Great Expectations data quality checks across 30 critical tables, reducing downstream reporting incidents by 65%"
- "Built Kafka Streams consumer processing 2M events/second for real-time fraud detection pipeline integrated with downstream ML scoring service"

Action verbs for data engineers: architected, automated, built, containerised, deployed, designed, developed, engineered, implemented, migrated, modelled, optimised, orchestrated, provisioned, scaled, transformed