Analytics Engineer
Owns the semantic and modeling layer of the data warehouse by transforming raw data into trustworthy datasets, reusable metrics, and documented data contracts. The role partners with domain and platform teams to support self-service analytics and efficient warehouse operations.
About the job
Responsibilities
- Build and maintain data models using modular, tested, and version-controlled practices.
- Partner with domain teams to understand business logic and codify it into reusable models and metrics.
- Define and document key metrics and data contracts across domains.
- Collaborate with Data Platform Engineers to optimize query performance and warehouse cost.
- Automate and maintain data documentation, lineage, and governance standards.
- Develop guidelines for analytics development, data modeling, and structure conventions.
Requirements
- Expertise with SQL, dbt, SQLMesh, or similar tools, including data modeling, testing, macros, and documentation.
- Experience with data warehousing concepts, cloud warehouses, and business intelligence tools.
- Understanding of dimensional modeling, data contracts, and metric or semantic layers.
- Familiarity with modern ELT and orchestration workflows.
- Strong business acumen and ability to translate domain logic into scalable data structures.
Technologies
- Cloud warehouses: Snowflake, BigQuery, Redshift, Databricks.
- BI tools: Looker, Tableau, Power BI, Hex, Metabase.
- Orchestration: Airflow, Dagster, Prefect.
- Cloud and infrastructure: GCP, AWS, Cloudflare, Terraform.
- Tooling: GitHub Actions, Grafana, OpenTelemetry.
Skills
SQL, dbt, Sqlmesh, Snowflake, BigQuery, Redshift, Databricks, Looker, Tableau, Power BI, Airflow, Dagster, Prefect, Terraform, GCP
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