Design, implement, and maintain complex data pipelines, ensuring scalability and reliability using Airflow, dbt, Rivery, Python, and SQL, enabling robust ingestion and transformation of structured and semi-structured data.
Serve as a strategic partner to business teams, working closely with stakeholders to translate high-level goals into data solutions that support forecasting, performance tracking, and optimization.
Develop and maintain clean, well-documented data models in Snowflake and BigQuery that support analytics, reporting, and operational workflows and contribute to architecture decisions.
Integrate data from a variety of internal and external sources, including Google Analytics and third-party APIs, to support full-funnel visibility across departments.
Enable self-service analytics by ensuring data assets are discoverable and usable via tools such as Tableau, including thoughtful semantic layer design and performance tuning.
Contribute to the development of robust monitoring and observability practices for data quality and pipeline health.
Collaborate on architecture and design decisions, including cloud infrastructure and containerization using AWS, Pulumi, and Docker.
Maintain strong documentation and promote engineering standards that ensure transparency, maintainability, and reusability of data systems.

7+ years of professional experience in data engineering, analytics engineering, or related roles.
Advanced proficiency in SQL and Python, with expertise in efficient query writing, data structures, and software engineering principles.
Hands-on experience with Snowflake and/or Big Query, including data modeling and performance optimization.
Proficiency with orchestration tools (e.g., Airflow) and data integration tools like dbt.
Experience working with cloud platforms, especially AWS, for data storage, compute, and infrastructure management, including services such as AWS Batch, ECR, Lambda functions, and related tools.
Familiarity with data analytics and visualization tools, particularly Tableau, and ability to support data consumers in building actionable dashboards.
Experience with marketing and product data sources, including Google Analytics and similar platforms.
Strong knowledge of ETL/ELT design and data warehousing solutions.
Familiarity with CI/CD pipelines and DevOps practices for data engineering.
Strong skills in Microsoft Suite for documentation and collaboration.
Robust experience with API design and integration.
Familiarity with SCRUM development methodologies and tools like Jira

Data Engineer III

Key skills