Data Engineer

We are looking for a highly skilled Data Engineer with a passion for modern data stack transformations. In this role, you will be the primary architect of our data transformation layer, moving away from legacy "black-box" ETL processes toward transparent, version-controlled, and testable code using dbt (data build tool).

dbt Expert knowledge of dbt Core/Cloud, including Jinja, Snapshots, and incremental models.
SQL Mastery of SQL (Snowflake, BigQuery, or Redshift dialects).ETL/ELT Tools Experience with ingestion tools like Fivetran, Airbyte, or custom Python scripts.Orchestration Familiarity with Airflow, Dagster, or Prefect for scheduling complex jobs.Warehousing Hands-on experience with cloud data warehouses (Snowflake, BigQuery, or Databricks).Software Skills Strong Git version control skills and familiarity with YAML configuration.

Key Responsibilities
Analytics Engineering: Design, develop, and maintain robust data transformation pipelines using dbt. Implement modularity, macros, and packages to streamline the modeling layer.

Data Modeling: Architect scalable data models using Star Schema or Data Vault 2.0 methodologies to ensure data is optimized for both storage and query performance.

Advanced SQL Development: Write, optimize, and tune complex SQL queries (Window functions, CTEs, Recursive queries) to extract insights from massive datasets.

ETL/ELT Workflow: Build and manage automated workflows that ingest data from various sources (APIs, SaaS tools, production DBs) into the cloud data warehouse.

Data Quality & Testing: Implement automated testing within dbt to ensure data integrity, freshness, and accuracy. Maintain documentation using dbt docs.

CI/CD & Version Control: Manage data codebases using Git, ensuring seamless deployments through automated CI/CD pipelines.

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