Manager – Data Engineering

We're seeking an experienced Data Engineering Manager, Business Intelligence to lead our high-impact team in designing and implementing star schema data models that serve both internal business intelligence needs and client-facing product insights. In this role, you’ll own the data architecture, champion data governance and quality, and drive the adoption of OpenMetadata as our Data Catalog, ensuring data is well-documented, discoverable, and trusted. You’ll mentor and develop engineers while working hands-on to transform complex business data into scalable, high-performing data products. This position blends technical leadership with deep stakeholder collaboration, shaping how both internal teams and customers leverage data for strategic decision-making.

Responsibilities: 

• Design and implement star schema data models across business domains and product analytics, ensuring dimensional modeling best practices
• Manage a team of data engineers, including hiring, mentoring, and professional development in a collaborative, high-performing culture
• Build ETL/ELT pipelines using Airflow that reliably populate fact and dimension tables in our Redshift environment
• Partner with product teams and business stakeholders to translate reporting requirements into effective data models that serve both internal analytics and customer-facing features
• Optimize data model performance for quick query response times in Tableau dashboards and product reporting interfaces
• Reduce time to insights by optimizing data pipelines, transformation logic, and data delivery processes, ensuring stakeholders and product teams have fast, reliable access to actionable data.
• Develop data transformations that enable accurate historical reporting while supporting intra-day data updates for real-time insights. Implement robust data lineage tracking to ensure transparency, traceability, and trust in data workflows, enabling stakeholders to understand the origin, transformations, and dependencies of key business metrics.
• Implement automated data quality checks, anomaly detection, and reporting to ensure stakeholders and customers trust and rely on our datasets.

Qualifications: 

• 6+ years of data engineering experience, including 2+ years in dimensional modeling and star schema design.
• 2+ years managing and mentoring data engineering teams, balancing leadership with hands-on contributions.
• Expert-level SQL skills, with deep experience in Amazon Redshift, Snowflake, or similar columnar databases.
• Strong experience with Apache Airflow for data pipeline orchestration, including scheduling, dependency management, and DAG optimization.
• Hands-on experience with ETL/ELT development, transforming raw data into structured, analysis-ready datasets using Python, dbt or other transformation frameworks.
• Proven ability to design high-performance data models, including slowly changing dimensions (SCD), fact tables, and surrogate keys for historical point-in-time analytics. 
• Experience implementing and managing a Data Catalog (OpenMetadata preferred) for governance and discoverability.
• Hands-on expertise in data quality testing, monitoring, and anomaly detection frameworks.
• Proficiency in Tableau (or similar BI tools) to optimize reporting performance and self-service analytics.
• Strong ability to translate business needs into scalable, reliable data solutions for internal stakeholders and product insights.
• Excellent communication and stakeholder collaboration skills, ensuring alignment across engineering, analytics, and business teams.

The base pay range for this position is $210,000-$230,000/year, which can include additional on-target commission pay/bonus. The base pay offered may vary depending on job-related knowledge, skills, and experience. Stock options and other incentive pay may be provided as part of the compensation package, in addition to a full range of medical, financial, and/or other benefits, depending on the position ultimately offered.