Lead, Analytics Engineer

Roles and responsibilities

• Architect, construct, and oversee data models within our Snowflake warehouse utilizing dbt.

• Take ownership of the transformation layer, converting raw source data into sets ready for advanced analytics.

• Collaborate with analysts and business stakeholders to identify requirements and develop scalable data models.

• Execute data quality assessments, monitoring, and documentation to maintain a high level of trust in our data.

• Establish and manage essential business metrics within a unified semantic layer.

• Enhance query efficiency and manage warehouse expenditure through optimization.

• Define and promote best practices for version control, CI/CD, and peer reviews for all analytics code.

• Provide mentorship to analysts regarding SQL best practices and core dimensional modeling principles.

• Play a contributing role in the refinement of our overarching data strategy and architectural framework.

Experience and qualifications

• Over 5 to 9 years of professional experience in analytics engineering, data engineering, or a comparable technical role.

• Advanced proficiency in SQL, with expertise in intricate transformations, window functions, and performance tuning.

• Direct experience using dbt or similar tools, alongside orchestration frameworks like Airflow.

• Solid grasp of data modeling methodologies, particularly with Snowflake schemas.

• Familiarity with cloud-based data warehouses such as Snowflake, BigQuery, Redshift, or Databricks.

• Skilled in Git and modern engineering workflows, including version control, code review, and CI/CD.

• Working knowledge of Python for developing scripts and automating processes.

• Experience with BI platforms, where knowledge of Tableau is considered a distinct advantage.

• Previous exposure to product analytics platforms like Amplitude, Mixpanel, or Snowplow is preferred.

  • Excellent communication skills with the ability to bridge the gap between business queries and data-driven solutions.