Responsible for scoping, architecting, designing, and developing robust data engineering solutions including data pipelines, data integration, and infrastructure.
Support the data architect in the creation of conceptual and logical data models.
Own the creation of physical data model optimized for analytics, reporting, and AI/machine learning use cases.
Serve as the technical owner of the data platform making architectural decisions, maintaining high code quality, and delivering scalable, reliable solutions.
Integrate data from diverse sources, including databases, APIs, flat files and cloud platforms.
Design, and build performant, scalable data pipelines using tools like dbt, Fivetran, and Airflow.
Troubleshoot issues with production data pipelines and implement monitoring and alerting as needed.
Collaborate across business, governance, QA, and analytics teams to ensure data quality, consistency, and successful solution delivery.
Define and implement enterprise scale data engineering best practices, standards and guidelines across the development life cycle.

Bachelors degree in computer science, data science, software engineering, information systems, or related quantitative field; masters degree preferred.
10+ years of Data Engineering experience, with at least 3 years in modern cloud/data stack.
Demonstrated experience designing and implementing enterprise scale data platforms.
Proficient in data management disciplines, including data integration, modeling, building data warehouses/lakes, and data quality, or other areas relevant to data engineering responsibilities and tasks.
Strong communication skills, to be able to clearly articulate technical concepts to non-technical stakeholders.
Strong problem-solving skills and a proactive, ownership-driven mindset.

Lead Data Engineer

Key skills