Sr. Data Engineering Manager - W2 only

Role: Senior Engineering Manager, Data Products

Duration: 3 months

Location: 100% remote

Key Responsibilities

• Manage delivery across multiple vendor engineering teams building data products across business domains.
• Own execution quality, delivery timelines, dependency management, issue resolution, and stakeholdercommunication.
• Review vendor designs and implementation approaches to ensure they follow approved architecture, engineering standards, and data product principles.
• Enforce dbt Core standards, including model structure, naming conventions, testing patterns, documentation, modularity, code review practices, and maintainability.
• Ensure strong data quality practices using dbt tests and other quality controls, with clear ownership of data validation, reconciliation, and defect resolution.
• Drive engineering rigor across ingestion, transformation, orchestration, semantic layer, cataloging, and consumption patterns.
• Partner with platform, architecture, business, and vendor teams to ensure each domain delivers reliable, governed, and scalable data products.
• Identify delivery risks early and drive mitigation plans across vendors and internal teams.
• Support the broader data mesh operating model by helping define clear domain ownership, reusable patterns, and consistent standards across data products.

Required Experience

• 10+ years of experience in data engineering, data platforms, analytics engineering, or enterprise data delivery.
• Strong experience managing vendor delivery across multiple teams, regions, and workstreams.
• Experience leading large-scale data engineering programs with accountability for delivery, quality, standards, and execution.
• 3+ years of hands-on dbt experience, with the ability to review dbt projects and enforce best practices.
• Strong working knowledge of AWS data services, including S3, Glue, IAM, Lake Formation, Lambda, managed Airflow, Redshift, and related data platform capabilities.
• Strong understanding of Apache Iceberg and modern lakehouse-style data architecture.
• Experience with Redshift-based data platforms and performance considerations.
• Experience with ingestion patterns including streaming, CDC/replication, and batch ingestion. Kinesis and
• Qlik Replicate experience is preferred.
• Strong understanding of data quality, testing, reconciliation, observability