We are looking for a Data Engineer with solid hands-on expertise in DBT (Data Build Tool) to design, build and maintain modular, tested and well-documented data transformation pipelines on a up-to-date cloud data stack. You will own the transformation layer end-to-end and work closely with analysts, product and business teams to deliver reliable, analytics-ready datasets.

Roles & Responsibilities:
Design, develop and maintain DBT models, macros, seeds and snapshots following modular and reusable design patterns (staging intermediate marts).
Implement DBT tests (generic, singular, custom), data quality checks and documentation; publish and maintain DBT docs and lineage.
Optimise SQL transformations on Snowflake for performance and cost warehouse sizing, clustering, result caching, incremental and materialisation strategies.
Build and manage incremental models, SCD Type 2 logic and snapshot-based historical tracking.
Manage DBT projects through Git-based version control, CI/CD pipelines and setting promotion (dev QA prod).
Work with Snowflake objects databases, schemas, roles, warehouses,



streams, tasks, external and internal stages.
Partner with data analysts and business stakeholders to translate requirements into scalable, well-modelled data marts (Kimball / dimensional modelling).
Monitor pipeline runs, troubleshoot failures, and drive root-cause analysis and permanent fixes.
Contribute to coding standards, peer code reviews and documentation practices within the data engineering team.

Required Skills (Must Have):
5+ years in data engineering / analytics engineering, with 3+ years of hands-on DBT (Core or Cloud) in production.
Expert-level SQL — window functions, CTEs, complex joins, query tuning.
Strong working knowledge of Snowflake — architecture, virtual warehouses, performance tuning and cost optimisation.
Jinja templating, DBT macros, packages (dbt_utils, etc.) and project structuring.
Git / GitHub / GitLab, branching strategies and CI/CD for data pipelines.

📌 Data Engineer Pune
🏢 Capgemini
📍 Pune