Data Engineer

We are looking for a Data Engineer with strong hands-on expertise in DBT (Data Build Tool) to design, build and maintain modular, tested and well-documented data transformation pipelines on a modern 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 environment 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.