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Data Engineer

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data-engineer.md

About the Role

You will design and build the data infrastructure that the rest of the company relies on for decisions. That means pipelines that move data from source systems into a warehouse, transformations that make raw data usable, and the tooling that keeps it all reliable. You care about data quality, pipeline uptime, and making sure analysts and ML engineers can trust the numbers.

Responsibilities

Design and build ETL/ELT pipelines that ingest data from APIs, databases, event streams, and file systems
Model and maintain a clean, documented data warehouse in Snowflake, BigQuery, or Redshift
Write and optimize SQL transformations using dbt or similar transformation frameworks
Build and manage data orchestration workflows with Airflow, Dagster, or Prefect
Implement data quality checks, anomaly detection, and alerting for pipeline failures
Collaborate with analysts and data scientists to understand data needs and deliver reliable datasets
Maintain documentation for schemas, lineage, and pipeline dependencies

Requirements

3+ years of data engineering experience building production pipelines
Strong SQL skills: window functions, CTEs, query optimization, schema design
Proficiency in Python for data processing and scripting
Experience with at least one modern data warehouse: Snowflake, BigQuery, or Redshift
Familiarity with orchestration tools: Airflow, Dagster, or Prefect
Understanding of data modeling concepts: star schema, slowly changing dimensions, event sourcing
Experience with cloud data services on AWS, GCP, or Azure
Clear written and verbal communication in English

Nice to Have

Experience with streaming data: Kafka, Kinesis, or Pub/Sub
Familiarity with dbt for SQL-based transformations and testing
Exposure to data governance and cataloging tools: Atlan, DataHub, or similar
Knowledge of Spark or other distributed processing frameworks

Tools & Workflow

Git and GitHub for version control of pipeline code and dbt models
VS Code or Cursor with AI-assisted development tools (GitHub Copilot or similar)
Airflow, Dagster, or Prefect for workflow orchestration
Jira, Linear, or similar for task tracking and sprint planning
CI/CD pipelines for automated testing and deployment of data transformations
dbt for SQL transformation development and testing
Snowflake, BigQuery, or Redshift as the primary data warehouse