Sr Data Engineer

MANDATORY SKILLS:

• Hands‑on experience designing and operating solutions in Snowflake, including virtual warehouse configuration, resource monitors, governance, and performance tuning.
• Expert SQL for analytics and transformation; skilled in query optimization, pruning, caching behavior, and result set reuse.
• Built robust pipelines into Snowflake with tools such as dbt, Airflow, dataops.live, Fivetran, Informatica, or AWS Glue; strong understanding of staging patterns, incremental loads, CDC, retries/error handling, and observability.
• Practical experience with dimensional and normalized models; proficient in schema design, standardization, clustering keys, micro‑partitioning, and workload/performance strategies.
• Experience in python for data engineering; integrating with serverless technologies such as AWS Lambda, and data integration services (AWS Glue).
• Experience with dbt modeling layers (staging, intermediate, marts), materializations (table, view, incremental, ephemeral), tests, and project configuration; able to enforce documentation standards and data contracts.
• Implemented data validation, unit/integration tests for transformations, and CI/CD for data pipelines; experience with monitoring and production support.
• Demonstrated track record delivering on modern data platforms (e.g., Snowflake, Redshift), distributed compute, and integration with analytics/visualization tools.
• Exposure across ingest and storage, preparation and transformation, discovery and presentation, and insight/consumption; able to translate business requirements into reliable data products.

· Strong understanding of FAIR data principles and data product best practices; able to design datasets with clear ownership, discoverability, standardized metadata/lineage, access controls, interoperability standards, versioning, and SLAs that meet real consumer needs.

• Comfortable with workflows (branching, merging, pull requests) and environment management.
• Strong analytical skills for diagnosing and resolving production issues.
• Ability to learn and apply relevant business processes to data solutions; collaborate with stakeholders to define SLAs and acceptance criteria.
• Effective working independently and in cross‑functional, cross‑cultural teams; clear communication of technical concepts to non‑technical audiences.
• Demonstrable passion for learning; familiarity with data and application design patterns; proactive in improving engineering practices.