Top Senior Data Engineer Jobs in Los Angeles, CA

Lead design and implementation of enterprise data architecture and cloud-based data solutions. Manage large data engineering projects, develop data models and pipelines, ensure governance and security compliance, advise clients strategically, and build high-performing inclusive teams.

Lead design and implementation of data architecture, ETL/ELT pipelines, data models, warehouses and lakes. Ensure data governance, security, cloud deployments, performance and cost optimization. Coach and manage team members, partner with stakeholders, and drive data-driven solutions for clients.

Design and build data infrastructure and pipelines using cloud and big-data tools. Develop data lakes/warehouses, ensure data quality and security, collaborate with clients, and apply data architecture, transformation, and analytics to deliver insights.

Design and implement data architecture and scalable ETL/ELT pipelines on cloud platforms (AWS/Azure/GCP). Analyze complex problems, mentor junior staff, ensure data governance/security compliance, liaise with stakeholders, and deliver high-quality client solutions while developing technical and client-facing skills.

The Data Engineer will architect and develop robust data frameworks for data platforms, focusing on data transformations and automated workflows. Responsibilities include collaborating with stakeholders, ensuring data quality, and maintaining high engineering standards.

As a Data Engineer, you will design, implement, and maintain data systems for Notifications at Whatnot, ensuring data quality and performance while collaborating with cross-functional teams on data architecture and workflows.

The Staff Data Engineer will design and build data pipelines, reports, and tooling for the Concepts Lab, ensuring reliable AI systems and leading data modeling and analytics efforts. The role requires extensive experience in production data systems and collaboration across teams to transition concepts into production.

Design, build, and optimize Snowflake data warehouses and data lakes; develop ETL/ELT pipelines and automated governance; support ML initiatives on AWS (Bedrock/SageMaker); ensure data quality, security, and cost-efficient platform operations.

Lead architecture and reliability of the data platform, build and harden ingestion (batch and CDC), own BigQuery+dbt transformations, establish testing/observability/governance, mentor engineers, develop Python tooling and APIs, run POCs for lakehouse/open table formats, and define AI-assisted development practices.

Design and build scalable batch and real-time data pipelines on Databricks and Azure. Implement ingestion, ETL, transformation, analytics workflows, APIs and data products to support AI/ML and BI. Ensure data quality, reliability, performance, and CI/CD-driven DevOps practices while collaborating with cross-functional teams.

Build and operate an enterprise Databricks lakehouse: design and maintain Bronze and Silver ingestion/transformation pipelines (batch/stream/CDC), implement Unity Catalog and secure platform constructs, create CI/CD for data assets, enforce data classification and quality, and collaborate with domain teams to enable governed data for analytics.

Lead design and scaling of the data platform and analytics pipelines. Build and maintain ETL/ELT workflows, integrations, serverless data services, and reliable data models. Mentor teammates, improve automation, observability, and data governance to support analytics and operational insights.

Own and evolve the data infrastructure powering identity and fraud products. Build and operate scalable ETL/ELT pipelines, ensure data quality and observability, optimize storage and access, support product squads, respond to production incidents, mentor engineers, and drive data platform innovation.

Lead the Data Platform Engineering team owning ETL/reverse-ETL pipelines, replication, and production datastores. Define architecture, roadmap, standards (IaC, observability, schema contracts), mentor engineers, drive incident response and durable fixes, and partner on cross-team technical decisions and operational reliability for data correctness, completeness, and timeliness.

Lead design and optimization of data models and pipelines for compliance and risk; standardize metrics and documentation; build data quality, lineage, and monitoring (including AI agents for automation); manage ETL scheduling, on-call pipeline support, and collaborate with product and non-technical partners to translate business needs into automated, production-ready data solutions.

As the founding engineer of the Data Intelligence team, you will design semantic layers, develop AI tooling, and automate data processes to drive business insights.

The Senior Data Engineer will design and implement scalable data models in BigQuery using dbt for analytics and reporting, ensuring data governance and optimal performance.

Lead design and delivery of scalable, secure cloud data pipelines and products. Translate requirements into architecture, apply advanced data modeling (Data Vault 2.0/dimensional), embed data quality, observability, security, and DataOps/CI-CD practices, optimize performance and cost, mentor engineers, and enable AI/ML-ready datasets within an agile DevSecOps model.

Design, build, and operate scalable cloud data pipelines and platforms (Azure preferred). Implement DevSecOps, CI/CD, automated testing, monitoring, performance tuning, and production readiness. Mentor engineers, drive operational excellence, and translate requirements into robust technical designs. Apply healthcare domain knowledge and collaborate in agile teams to deliver secure, reliable data products.

The Senior Data Engineer will design and build data pipelines, manage data architecture, ensure privacy compliance, mentor team members, and collaborate with cross-functional teams.

Build and maintain batch ETL pipelines on Azure Databricks and Microsoft Fabric to ingest Workday and legacy systems into a governed medallion lakehouse. Develop Spark/Python/SQL transformations across Bronze/Silver/Gold layers, orchestrate and monitor workflows, implement data-quality checks, and model data for analytics. Lead-level engineers set standards, own complex integrations, mentor peers, and partner on platform and governance decisions.

Design, build, and maintain scalable data architectures and pipelines (databases, data warehouses, data lakes) to support analysts and data scientists. Implement ETL/data integration, ensure data security and governance, optimize retrieval and performance, collaborate with stakeholders, and deploy solutions using cloud platforms and modern data processing frameworks.

Lead design and operation of large-scale data pipelines and data APIs. Build Spark/PySpark workflows on Databricks, optimize job performance, manage data quality and observability, and develop MCP servers and AI-agent integrations. Mentor engineers, define standards, support production incidents and on-call rotations, and collaborate with stakeholders to deliver scalable data platform products.

Lead architecture and buildout of a new graph-backed enterprise data platform: design ingestion, graph and relational storage, entity resolution pipelines, temporal models, ETL/ELT pipelines, governance, APIs, and production connectors. Ship scalable graph data models, traversal queries, and platform roadmap while enabling observability, security, and containerized deployments.

As a Data Engineer, you will develop solutions for customer-centric technology, ensuring platform integration and managing data architecture. Responsibilities include building data processes, maintaining data integrity, and collaborating cross-functionally to meet business challenges.