Senior Data Engineer
Tasks
- Define conformed dimensions and operational facts
- Design and build Snowflake transformation layer
- Design data for downstream AI ML and agent consumption
- Implement data quality testing observability and lineage
- Migrate SQL views to dbt models
- Own semantic layer for BI and AI use cases
- Partner on SOX relevant controls for financial data flows
- Set dbt modeling testing naming documentation and CI CD standards
- Support semantic equivalence during data cutover
Perks/Benefits
Skills/Tech-stack
Airbyte | Airflow | Azure Data | Azure Data Factory | Azure DevOps | Azure DevOps Pipelines | CI/CD | DAX | DBT | DBT Cloud | Dagster | Data Factory | Data Lineage | Data Quality | Data Quality Testing | DevOps Pipelines | Dimensional Modeling | Elementary | Embeddings | Fivetran | GitHub Actions | GitLab CI | Incremental loading | Informatica | Kimball Dimensional | Kimball dimensional modeling | LLM | Matillion | Model contracts | Power BI | Python | Quality testing | RAG | SCD Type 1 | SCD Type-2 | SQL | Semantic layer | Semantic metadata | Snowflake | Star Schema | Surrogate keys | Type 1 | Type 2) | Window Functions
Education
Bachelor of Engineering | Bachelor of Science | Master of Science | PhD
Roles
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