Build and maintain robust, scalable data pipelines using Airflow, Python, and SQL.
Design, build, optimize data pipelines using dbt, or sqlmesh.
Develop and manage the ODS and support the eventual rollout of a modern Data Warehouse.
Integrate data from internal systems and external APIs to create clean, reliable datasets.
Work closely with engineers to operationalize machine learning workflows.
Ensure high data quality through monitoring, validation, and error handling.
Provide guidance to less experienced team members and champion data engineering best practices.
Deploy and manage infrastructure in the cloud (AWS, GCP, or Azure) using modern DevOps tooling.
Implement monitoring and alerting to ensure data pipelines are reliable and maintainable.

At least 5 years of experience in data engineering or related roles.
Strong skills in Python, SQL, and Airflow or similar orchestration tools.
Experience working with cloud infrastructure and data warehousing tools (e.g., Snowflake, BigQuery, Redshift).
Exposure to ML pipelines or collaboration with ML/AI teams.
Ability to work independently while supporting a less-experienced team.
Strong communication skills and an eagerness to mentor and share knowledge.
Experience building an ODS or Data Warehouse from scratch (desirable experience).
Familiarity with event-driven systems or streaming tools (e.g., Kafka, Pub/Sub) (desirable experience).
DevOps experience or infrastructure-as-code (e.g., Terraform, CloudFormation) (desirable experience).

Data Engineer

Key skills