Engineering Lead, Data Foundations

Swinerton•Concord, CA

1d•$160,000 - $190,000

About The Position

The Engineering Lead, Data Foundations leads the design and delivery of trusted, scalable data foundations that power enterprise BI, analytics, and AI. This hands-on engineering leadership role is responsible for establishing architecture, standards, governance practices, and data products that enable trusted decision-making, enterprise analytics, and AI-powered solutions across the enterprise. The role owns the enterprise data foundations strategy and roadmap, partners with business, analytics, and platform stakeholders, and translates needs into scalable architecture and implementation plans. It builds data products, semantic layers, serving models, and graph-based knowledge structures, while establishing practices for governance, lineage, master data management, data quality, stewardship, and access. The role also sets reusable engineering standards and leads, coaches, and develops a team of engineers while contributing directly to design and code reviews and complex engineering work.

Requirements

Bachelor’s degree in Computer Science, Engineering, Information Systems, Data Science, or a related field.
At least 5 years of experience in data engineering, analytics engineering, BI architecture, data platforms, or related technical roles, including experience leading technical teams or delivery efforts.
Strong expertise in SQL and modern data engineering practices, including designing scalable data products, serving layers, and analytics-ready data structures.
Experience with modern cloud data platforms and lakehouse or warehouse ecosystems such as Databricks, Snowflake, BigQuery, Azure data services, or equivalent technologies.
Experience establishing data governance, lineage, data quality, metadata, access controls, and related monitoring or observability practices.
Experience defining engineering standards, documentation practices, CI/CD approaches, and reusable delivery patterns for enterprise data solutions.
Experience translating business needs into scalable data architecture approaches, implementation plans, and well-scoped technical work.
Strong communication and stakeholder management skills, including explaining technical tradeoffs, aligning cross-functional teams, and connecting engineering work to business value.

Nice To Haves

An advanced degree.
Familiarity with semantic technologies, knowledge graphs, graph data modeling, or machine-readable metadata patterns that improve business context and AI readiness.
Experience building or supporting data foundations for BI, analytics, AI, search, retrieval, or unstructured data use cases.
Construction industry experience.

Responsibilities

Own the enterprise data foundations strategy by defining architecture, standards, semantic and knowledge layers, and a roadmap for governed data assets supporting BI, analytics, automation, and AI use cases.
Partner with business, analytics, and platform stakeholders to translate needs into scalable data solutions and align foundational capabilities with measurable business outcomes.
Translate business and platform needs into scalable data architecture approaches, implementation plans, and well-scoped work.
Build scalable data products, semantic layers, and serving models that combine data modeling, master data, business context, metadata, and graph-based knowledge structures.
Establish trusted data foundations through governance, lineage, master data management, data quality, stewardship, and access practices.
Set engineering standards for data modeling, schema evolution, CI/CD, documentation, and reusable patterns.
Lead, coach, and develop a team of engineers, supporting prioritization, delivery execution, issue resolution, and ongoing growth.
Contribute to design reviews, code reviews, and resolution of complex engineering issues to ensure solution quality, reliability, and production readiness.