DBT Data Engineer (hybrid)

NTT DATA Services•Upper Merion Township, PA

1d•$115,000 - $125,000•Hybrid

About The Position

The DBT SME serves as the senior engg for data transformation within a modern data stack built on dbt, Snowflake. This role is responsible for implementing dbt transformation models, enforcing modeling best practices, and ensuring that dbt, and traditional data modeling disciplines work together cohesively in a production environment. The ideal candidate brings hands-on dbt expertise, deep Snowflake knowledge, strong Python skills, and the ability to translate conventional data warehouse patterns into maintainable, scalable dbt solutions.

Requirements

5+ years of DBT Core experience — model layering, macros, Jinja templating, incremental materializations, snapshots, and dbt tests.
3+ years of hands-on Snowflake experience as a dbt target, including SQL dialect proficiency, query optimization, clustering, and RBAC.
3+ years of Python experience applied to data engineering, including pipeline scripting, automation, and data quality tooling.
5+ years of SQL experience — complex joins, window functions, CTEs, and aggregations across large datasets.

Responsibilities

Design and develop dbt transformation models targeting Snowflake, applying model layering (staging, intermediate, mart), incremental strategies, snapshot patterns, and dbt macros.
Apply Snowflake-specific optimizations within dbt models, including clustering keys, transient tables, and appropriate warehouse sizing to control cost and performance.
Translate traditional data warehouse designs — including dimensional models, ERDs, and legacy SQL transformation logic — into dbt-native patterns.
Develop and maintain dbt tests (generic and singular), custom Python-based data quality checks, and documentation to ensure transformation correctness and pipeline observability.
Perform peer code reviews, provide technical mentorship to engineers, and drive adoption of dbt best practices and modeling standards across the team.
Troubleshoot data discrepancies, pipeline failures, and Snowflake query performance issues, and implement preventive measures.
Maintain technical documentation, transformation runbooks, and handover materials.