Top Data Engineer Jobs in San Diego, CA

Lead architecture and scaling of the Databricks data warehouse and self-service data platform. Build reusable batch and streaming frameworks, enforce governance, SLOs, lineage, observability, CI/CD, and multi-tenancy. Mentor engineers, run design reviews, and ship documented patterns to enable analytics, data science, and app teams to move quickly without blocking on platform tickets.

Design, build, and maintain backend systems and data interactions for healthcare reporting. Develop APIs, data models, ETL pipelines, and interfaces; optimize performance, ensure data integrity and security, and support automated testing and CI/CD. Collaborate in Agile teams and reduce technical debt while enabling integrations with frontend and third-party systems.

Build end-to-end data and analytics features: ingest and model data, build transformations and pipelines, create client-facing dashboards and interfaces, and develop LLM-powered workflows to accelerate analysis and app development. Partner with stakeholders, perform exploratory analyses, and ensure data quality, observability, and maintainability while influencing data engineering strategy.

Lead design, build, and maintain scalable batch and streaming data pipelines and data warehouse models. Ensure data quality, observability, performance, and security. Own workstreams, support production incidents, and enable analysts and downstream applications.

Administer, stabilize, and scale Oura's global Databricks environment. Manage workspace governance, cluster policies, access control, cost monitoring, job scheduling, and incident SLAs. Onboard teams, resolve performance and reliability issues, and partner with IT and Data Architecture. Over time contribute to pipeline development, Spark optimization, CI/CD, and infrastructure-as-code to support domain-oriented data mesh initiatives.

Lead the vision and execution for scalable data governance and quality across Airbnb. Architect future-proof data ecosystems, define best practices and tooling, drive cross-functional policy implementation (privacy, legal, infosec), mentor senior engineers, and influence executive strategy to embed stewardship into the data product lifecycle.

Design, build, and operate large-scale AWS-based ETL pipelines using Glue/PySpark to ingest, transform, and curate multi-format data into Apache Iceberg tables. Ensure data quality, metadata management, semantic layer creation (Trino/Athena), CloudFormation deployments, documentation, and compliance within a FedRAMP federal agile environment.

Serve as a data quality and QA engineer on a federal data platform: build automated AWS Glue/Deequ data quality checks, validate schemas and ETL pipelines, create and run unit/integration tests with 90% coverage, support IV&V and UAT, perform static/security scans, produce test documentation, monitor production ETL, and collaborate with data stewards and IV&V teams.

Define and lead the technical architecture for a lakehouse on AWS, build production pipelines and data models for time-series and multi-source data, introduce data governance and catalog foundations, author platform playbooks (ADRs, runbooks, Terraform modules), deliver projects end-to-end, and mentor engineers while representing the platform to senior leadership and non-technical stakeholders.

Design and build scalable, semantically consistent 360 entity data models and transformation pipelines. Implement cleansing, enrichment, KPIs, scoring, and business rules in SQL and Python/Scala, ensure data quality, lineage, and performance at scale. Use AI coding assistants and SDD to accelerate development, create tests and documentation, and collaborate with domain experts, data scientists, and product teams to deliver trusted, reusable data assets.