Data Engineer

Stakeholder Engagement & Requirement Gathering: Partner with business stakeholders across Analytics, Operations, GTM, and G&A to understand problem statements and translate them into clear data requirements..

Data Modeling & Transformation: Design and build dimensional and analytical data models in Snowflake using dbt (Cloud/Core) that serve as the single source of truth for business domains. Develop, test, and maintain dbt models following best practices—including modular layering (staging, intermediate, marts), documentation, and data tests. Optimize SQL queries and dbt models for performance, leveraging Snowflake features such as clustering, materialization strategies, and query profiling.

Data pipeline Orchestration: Build and manage Airflow DAGs to orchestrate multi-step data pipelines, ensuring reliability, observability, and SLA adherence. Own end-to-end delivery of data products—from raw source data ingestion through transformation to governed, consumption-ready datasets. Collaborate with the Data Platform team on pipeline integration, CI/CD workflows, and adherence to shared coding and deployment standards.

• Data Quality & Analysis: Implement data quality checks and freshness tests within dbt to proactively catch issues before they reach consumers. Perform exploratory data analysis to validate source data, understand distributions, and surface data anomalies during scoping and build phases. Work with stakeholders to define and monitor data SLAs, ensuring data products are trustworthy and consistently delivered.