Manager, Data Platform & Engineering

TheKey

About The Position

For nearly 20 years, TheKey has helped clients achieve successful long-term aging at home with comprehensive, concierge-based care. Ensuring the dignity, safety, and independence of its clients, TheKey is committed to changing how the world lives and ages at home. Employee-teams get the training, resources, and support they need to deliver an exceptional care experience for clients and their families. Founded in Silicon Valley, TheKey has grown from a single location to service coverage throughout North America enabling clients to live life on their own terms, in their own homes.

Requirements

Bachelor's or Master's degree in Computer Science, Information Systems, Data Engineering, or a related field.
5+ years of experience in data engineering, with at least 1–2 years in a team lead or people management capacity.
Hands-on expertise with Google BigQuery and the broader Google Cloud data ecosystem (Dataflow, Pub/Sub, Dataplex, Looker, etc.).
Demonstrated experience building and managing cloud-native data pipelines and ELT/ETL processes at scale.
Working knowledge of AI/ML workflows and what it takes to prepare data for model training, RAG pipelines, or AI-powered analytics.
Experience implementing data catalogs, metadata frameworks, and data lineage tooling.
Familiarity with AI-assisted development tools and a track record of integrating them into engineering workflows.
Strong data governance background including security, compliance, and quality frameworks.
Strong communication and stakeholder management skills.

Nice To Haves

Google Cloud Professional Data Engineer certification.
Experience with dbt (data build tool) for transformation layer management.
Familiarity with vector databases or embedding pipelines in support of generative AI use cases.
Experience with DataOps practices and pipeline observability tooling.
Exposure to agentic AI frameworks or LLM orchestration tools (Vertex AI Agent Builder, LangChain, etc.).

Responsibilities

Contribute to and execute a roadmap that positions TheKey's data platform as AI-ready — well-structured, richly documented, and accessible for machine learning and generative AI use cases.
Champion the adoption of AI and automation tools within the data engineering team to increase delivery velocity and reduce manual effort.
Partner with business and technology stakeholders to align platform capabilities with analytics and AI initiatives.
Lead the design and governance of a metadata-rich data lake in Google BigQuery, ensuring datasets are tagged, documented, and contextualized so AI models and analytics tools can effectively interpret and utilize the data.
Establish and enforce standards for data cataloging, semantic tagging, lineage tracking, and business glossary definitions to maximize AI discoverability and usability.
Drive adoption of tools such as Google Dataplex or equivalent for automated metadata management and data quality enforcement.
Build and maintain scalable data pipelines, leveraging AI-assisted development tools (e.g., Gemini Code Assist, GitHub Copilot, or similar) to accelerate development and reduce errors.
Implement AI-driven testing and observability frameworks to automatically validate pipeline outputs, detect anomalies, and enforce data quality — reducing reliance on manual QA.
Automate repetitive data engineering workstreams — including ingestion, transformation, and delivery — using orchestration tools and AI-assisted scripting.
Define and enforce data governance frameworks that support both regulatory compliance and AI readiness — including data quality standards, access controls, retention policies, and privacy requirements.
Ensure all data assets are properly classified, documented, and governed so they can be safely and effectively used in AI and analytics applications.
Proactively identify and reduce technical debt in legacy data processes and structures.
Own master data management practices to ensure accuracy, consistency, and a single source of truth for critical business entities.
Enrich master data with metadata and contextual attributes that improve usability for downstream AI and reporting tools.
Directly manage a team of data engineers — providing mentorship, clear expectations, and career development support.
Foster a culture of continuous learning, experimentation with AI tools, and engineering excellence.
Conduct regular 1:1s, performance reviews, and workload prioritization to keep the team focused and productive.