Engineering Lead Manager, Data Products

Role overview

Team leadership and delivery

• Lead, mentor, and grow engineers; establish practices for architecture, testing, reliability, observability, and operational ownership.
• Partner with Product and Engineering leaders on a scalable roadmap, delivery priorities, architecture, automation, and reliability investments.
• Coach senior engineers to lead complex initiatives and translate business needs into technical direction.

Data products and real-time systems

• Build curated and aggregated datasets for reporting, billing, operations, experimentation, and machine-learning use cases.
• Create reusable frameworks and patterns; own correctness, reconciliation, freshness, and SLAs for product-facing datasets.
• Build streaming-derived products, business-oriented transformations, and architectures combining historical and real-time data.

APIs, analytics, and integrations

• Develop domain, reporting, retrieval, and serving APIs with clear contracts between data producers and consumers.
• Modernize customer-facing and operational reporting, embedded analytics, dashboards, and troubleshooting experiences.
• Build integrations and distribution with APIs, streams, connectors, webhooks, external delivery, data sharing, and Reverse ETL.

AI, ML, and production ownership

• Develop datasets and analytical products for ML workflows, experimentation, approximate analytics, and AI-enabled data access.
• Own reliability, SLA adherence, observability, recovery workflows, correctness, reconciliation, capacity, releases, and incident response.
• Increase reuse, self-service, freshness, adoption, and business impact while reducing repetitive engineering work.

Technical experience

• Strong experience building and operating large-scale distributed or data-intensive systems; strong knowledge of data processing, APIs, and production system design.
• Experience with Kafka, Spark, Flink, Kubernetes, Airflow, or comparable systems; backend APIs in Go, Java, Python, or similar.
• Experience with batch and streaming architectures, data modeling, aggregation, partitioning, schema evolution, correctness, and distributed processing.
• Experience designing reliable systems with SLAs, observability, operational ownership, and reusable platforms or abstractions.
• Large-scale analytics, advertising technology, experimentation, ML infrastructure, customer-facing data products, or data integration platforms are a strong plus.

Leadership experience

• Experience leading engineers responsible for complex production systems and mentoring senior engineers.
• Strong technical judgment and ability to lead architecture and system-design discussions and balance delivery, technical debt, reliability, and long-term investments.
• Strong cross-functional leadership, communication, and ability to turn ambiguous business problems into clear technical direction.

Benefits

• Benefits include health, dental, and vision plans; paid time off; retirement matching; equity; parental leave; wellness and commuter benefits; and learning resources.

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