Responsibilities
- Design, build, and evolve scalable ETL/ELT pipelines for high-volume, fast-changing datasets
- Own medium-to-large data initiatives end-to-end, from requirement shaping and technical design to production rollout and ongoing improvement
- Model reliable, analytics-ready data using proven warehouse methodologies such as Kimball, Inmon, or similar approaches
- Develop and maintain curated analytical layers, semantic models, and data marts that enable self-service and trusted decision-making
- Orchestrate robust workflows using tools such as Apache Airflow and improve reliability, observability, and maintainability of data pipelines
- Partner with software engineers, analysts, product managers, and business stakeholders to translate ambiguous requirements into scalable data solutions
- Proactively identify data quality, performance, and modeling issues and drive improvements before they become business problems
- Contribute to architecture and engineering decisions within your domain, balancing speed, quality, and long-term maintainability
- Mentor less senior data engineers through code reviews, design discussions, documentation, and day-to-day guidance
- Raise the engineering bar through testing, automation, CI/CD practices, clear documentation, and thoughtful technical standards
Requirements
- Strong SQL and Python skills, with solid hands-on experience building production-grade data pipelines and analytical models
- Deep understanding of dimensional modeling methodologies and modern data warehouse design in platforms such as Snowflake, BigQuery, or similar
- Experience owning projects independently and driving delivery across multiple stakeholders or teams
- Solid experience with orchestration tooling such as Airflow, Dagster, or similar
- Strong understanding of data quality, lineage, observability, and operational excellence in a data engineering environment
- Ability to take ambiguous business problems and turn them into clean, maintainable, well-documented data solutions
- Experience working with cloud platforms, preferably AWS, though Azure or GCP experience is also valuable
- Confidence influencing technical decisions within your team and collaborating effectively with both technical and non-technical stakeholders
- Strong communication skills and a collaborative, problem-solving mindset
Nice to Have
- Experience with event-driven source systems
- Experience with cloud-native analytical databases like Snowflake, BigQuery, Databricks
- Familiarity with semantic layers, metrics modeling, and enabling data products beyond traditional BI reporting
- Exposure to experimentation-driven or product-led environments
- Experience with data governance, cataloging, and documentation practices such as DataHub
- Experience improving developer productivity through tooling, shared frameworks, or reusable patterns
- Multi-cloud exposure and understanding of platform trade-offs
Team
Team size: 40+. Structure: data engineers
