Top Data Engineer Jobs in Los Angeles, CA

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.

Design and build data infrastructure, pipelines, and integration solutions using cloud and big-data tools. Develop data lakes/warehouses, ensure data quality and security, apply data modeling and DAGs, use Databricks, Airflow, and Hadoop, and collaborate with clients to deliver actionable insights.

Lead design and implementation of data infrastructure, pipelines, and integrations using cloud platforms. Manage teams and client accounts, ensure data quality, security, and compliance, deploy scalable solutions (Databricks, Snowflake), mentor junior staff, and identify data-driven business opportunities.

Design and maintain data collection infrastructure and resilient pipelines, troubleshoot and resolve data defects, implement maintenance strategies, support BI/warehousing/reporting, build reports and visualizations, and collect/extract/clean data end-to-end, including real-time streaming and performance tuning.

As a Senior Associate in Data Engineering at PwC, you will design and implement data architecture strategies, analyze complex issues, mentor junior members, and build client relationships while ensuring adherence to project standards.

As a Senior Manager in Data Engineering, you will lead teams to design data architectures, develop data solutions, manage client engagements, and ensure compliance with data governance policies.

Lead data engineering projects, develop and implement data pipelines and architecture strategies, manage data quality, and mentor team members at PwC.

As a Data Engineer, you'll lead data engineering projects, develop data transformations, build workflows with Apache Airflow, and ensure data quality and integrity while collaborating with stakeholders.

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.

The Solutions Data Engineer will manage data integrations end-to-end, utilizing FloQast's Data Platform and Data Studio to connect source systems and transform data for reliable outputs across applications.

The Data Engineer will build and maintain data pipelines, optimize workflows, manage infrastructure, and enhance pipeline observability while collaborating with teams for effective data delivery.

The Data Engineer will help build modern data infrastructure, designing ETL pipelines, modeling datasets, and collaborating with teams for improved data quality and accessibility.

Build and maintain reliable data pipelines, Airflow DAGs, and Snowflake-based Data Vault/warehouse models. Implement CI/CD, automated testing, observability, and production support while partnering with stakeholders and developing insurance domain expertise.

Design and lead development of backend architectures, full‑stack applications, and data modeling to enable performance marketing campaigns. Own standards for engineering, testing, deployment, and monitoring; collaborate with data scientists and marketers to implement safe, data-driven campaign workflows and automation.

Design, build, and optimize GCP-based data architectures and ETL/ELT pipelines using BigQuery, Dataflow, dbt, and orchestration tools. Ensure data quality, security, and integration from diverse sources; collaborate with stakeholders to deliver actionable insights.

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.

As a Data Engineer, you will build and scale data pipelines, optimize advertising effectiveness, and handle large datasets using AWS, Airflow, and Snowflake.

As the first in-house Data Engineer, you will own the data infrastructure, build and maintain data pipelines, and collaborate with teams to ensure quality data access and insights.

Design, build, and maintain scalable Spark-based ETL pipelines and computed tables in a central data lake. Integrate structured and unstructured IoT, sensor, and external data for analytics, model training, and dashboards. Collaborate with Data Science, Analytics, and ML teams to ensure reliable, high-quality customer-facing datasets.

Design, build, and operate large-scale data platforms and Spark/PySpark pipelines. Enable data integration, modeling, quality, and observability. Build MCP servers and AI-augmented tooling, mentor engineers, and lead cross-functional projects to deliver reliable data products.

Own and architect large-scale data pipelines, orchestration, and integrations. Drive IaC strategy (Terraform), third-party API and AI/LLM tool integrations, build export pipelines and internal tooling, mentor engineers, and align cross-team technical strategy for reliable, scalable data infrastructure.

Build and maintain cloud cost monitoring, alerting, and optimization tooling. Develop automation, cost allocation/tagging, data pipelines, dashboards, and integrations with billing APIs. Support FinOps processes, implement cost guardrails, troubleshoot cost systems, participate in on-call rotation, and collaborate with senior engineers and product teams to drive cost-efficient infrastructure and reporting.

Design, build, and maintain data pipelines and backend systems to automate reporting and support cross-functional teams. Own project prioritization, collaborate with stakeholders (Accounting, Finance, Supply Chain, Publishing), and iterate to improve cost, reliability, and access to data across the organization.

The job involves leveraging AWS and Snowflake for data engineering tasks within a technology-focused company, requiring US residency.

Design, build, operate, and maintain production ETL/ELT pipelines, integrations, and curated datasets across enterprise systems (Salesforce, Deltek, Autodesk, Azure, Microsoft Fabric). Implement data models, orchestration, API-first services, monitoring, data quality, lineage, and governance to support BI, reporting, AI/ML, and business decision-making. Collaborate with analytics, IT/security, and business teams to ensure secure, reliable data delivery.