Data Engineering Architect, Senior

Primary Responsibilities:

You are a data engineer who thrives in a highly collaborative environment, partnering with product, analytics, and engineering teams to deliver high-quality, trusted data. You're motivated by building scalable data systems and shaping how data is modeled, governed, and consumed across a modern cloud platform. You bring deep, hands-on data engineering experience and are equally comfortable designing future-state architecture, building production solutions, and establishing the patterns and standards that allow others to build effectively.

You bring recent, hands-on production experience with Databricks and will play a leading role in evolving our Databricks-based Lakehouse architecture, including the modernization and migration of existing data workloads. You enjoy translating complex product and user behavior into well-structured, reliable datasets that power analytics, experimentation, and decision-making. You can move comfortably between technical implementation and strategic architecture, communicating complex decisions clearly and influencing technical direction across teams.

You bring recent, hands-on production experience with Databricks and will play a leading role in evolving our Databricks-based Lakehouse architecture, including the modernization and migration of existing data workloads. You enjoy translating complex product and user behavior into well-structured, reliable datasets that power analytics, experimentation, and decision-making. You can move comfortably between technical implementation and strategic architecture, communicating complex decisions clearly and influencing technical direction across teams.

Partner with product analytics stakeholders to translate business-defined KPIs and data requirements into scalable, production-grade datasets. Own the design, build, and operation of scalable data pipelines end-to-end (ingestion → transformation → serving). Define and evolve the architecture of the Product Analytics Lakehouse, making technical decisions that improve scalability, performance, reliability, governance, and consistency across datasets and workloads. Build and maintain production-grade, well-modeled datasets (Gold layer) that power analytics and AI use cases.

Define, implement, and drive adoption of reusable data engineering patterns, frameworks, standards, and guardrails that reduce duplication, improve engineering leverage, and make the right development patterns easier to adopt. Own data quality and reliability for production datasets, including validation, monitoring, SLAs, and incident resolution. Productionize and scale prototype datasets and logic developed by analytics partners into reliable, maintainable data pipelines.

Build governed, purpose-built datasets to support AI/ML use cases while enforcing controlled and secure data access patterns. Lead the technical evolution of workloads into Databricks, evaluating existing architecture and determining appropriate migration, modernization, and coexistence strategies. Make and communicate architectural tradeoffs across performance, cost, reliability, governance, maintainability, and developer experience. Provide technical leadership and architectural guidance across Product Analytics, helping engineers and analytics partners make sound data architecture, modeling, and platform decisions.

Mentor and provide technical guidance to engineers and other technical contributors, raising engineering standards through hands-on leadership rather than formal authority.

Strong experience building and operating data pipelines using SQL and Python in a modern cloud environment. Deep expertise in SQL, including complex transformations, data modeling, query optimization, and performance tuning at scale. 2+ years of recent, hands-on production experience with Databricks, including designing, building, optimizing, and operating production data workloads. Strong hands-on experience with Spark/PySpark and distributed data processing in a production environment.

Strong understanding of modern data architecture patterns, including Lakehouse architecture, ELT, and layered data models…