Build, maintain, and optimize data pipelines for batch and streaming workloads.
Develop reliable data models and transformations to support analytics, reporting, and operational use cases.
Integrate new data sources, APIs, and event streams into the platform.
Implement data quality checks, testing, documentation, and monitoring.
Write clean, performant SQL and Python code.
Contribute to improving performance, scalability, and cost-efficiency across the data platform.
Work closely with senior engineers to implement architectural patterns and best practices.
Collaborate with analysts and data scientists to translate requirements into technical solutions.
Participate in code reviews, design discussions, and continuous improvement initiatives.
Help maintain clear documentation of data flows, models, and processes.
Support the adoption and roll-out of new data tools, standards, and workflows.
Contribute to DataOps processes such as CI/CD, testing, and automation.
Assist in monitoring pipeline health and resolving data-related issues.

2–5+ years of experience as a Data Engineer or similar role.
Experience with dbt (or similar) —model development, tests, documentation, and version control workflows.
Strong SQL skills for data modeling and analysis.
Proficiency with Python for pipeline development and automation.
Experience working with orchestration tools (Airflow, Dagster, Prefect, or equivalent).
Understanding of ETL/ELT design patterns, data lifecycle, and data modeling best practices.
Familiarity with cloud environments (AWS, GCP, or Azure).
Knowledge of data quality, observability, or monitoring concepts.
Good communication skills and the ability to collaborate with cross-functional teams.

Data Engineer

Key skills