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Tier-1 brand, mid-level common data-engineer role with broad tooling increases candidate competition.
Core data engineering skills (dbt, Snowflake, Airflow, SQL) are highly transferable across industries.
Multiple mandatory skills (dbt, Snowflake, Airflow, SQL, CI/CD) and 6+ years make shortlisting strict.
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Lead data transformation and analytics engineering using tools like dbt to build and maintain sophisticated, high-quality data models supporting business intelligence and analytics.
Design, implement, and optimize data pipelines and workflows using CI/CD pipelines and orchestration tools such as Airflow or Dagster to ensure scalable and maintainable data architectures.
Collaborate across teams to meet data requirements, troubleshoot performance bottlenecks, and contribute to data management strategy and architecture.
6+ years of experience in data engineering with a focus on data transformation and analytics engineering.
Strong proficiency in SQL and experience with dbt or similar data pipeline/transformation tools (Glue, FiveTran, Alteryx).
Experience with CI/CD pipelines (GitHub Actions, Azure DevOps, GitLab) and version control best practices.
Familiarity with cloud-based data warehouses such as Snowflake, Redshift, or Azure and experience using orchestration tools like Airflow or Dagster.
Experienced in designing scalable and maintainable data architectures aligned with engineering standards and long-term growth.
Capable of mentoring junior engineers and sharing best practices in data engineering, data modeling, and pipeline development.
Experienced in cross-functional collaboration with data scientists and visualization teams to deliver accessible, high-quality data for analytics and reporting.