Top Data Engineer Jobs in Los Angeles, CA

Leads enterprise-scale data architecture across batch, streaming, warehouse, lakehouse, transactional, AI, and vector-store use cases. Establishes data quality, governance, lineage, observability, access-control, and reliability standards. Defines AI-enabled data engineering patterns, mentors engineers, resolves complex architecture issues, and influences cross-functional roadmaps. Requires hands-on technical leadership and 15+ years of software, data engineering, or architecture experience.

Build and deploy scalable data pipelines for ingestion, transformation, quality assurance, migration, and automated data consumption. Apply cloud, big data, DevOps, and CI/CD practices while developing data governance processes, quality rules, compliance policies, and stewardship workflows. Collaborate with business and BI teams to define requirements, document technical designs, and support finance-related data solutions. The role may require significant client travel.

Architects, deploys, and operates scalable data platform infrastructure supporting analytics. Responsibilities include administering Airflow and Superset, managing Terraform infrastructure, building CI/CD pipelines, implementing security and compliance controls, developing dbt data models, cataloging assets in OpenMetadata, automating with Python, and mentoring engineers. The role requires extensive infrastructure and data engineering experience, cloud expertise, container orchestration, and eligibility for CJIS clearance.

Designs, implements, and sustains secure mission data pipelines for the USSF Warp Core platform. Responsibilities include mission data architecture, schema and interface definition, ETL and transformation development, API integration, monitoring, and data validation. The role also supports DoD RMF compliance, cybersecurity alignment, accreditation artifacts, technical documentation, and interoperability across mission systems. Requires collaboration with Space Systems Command, government partners, cybersecurity teams, and multi-agency stakeholders.

Build and maintain analytical datasets, dimensional models, Power BI semantic models, and governed data transformations in Microsoft Fabric. Translate business requirements into reliable data products, perform testing and reconciliation, troubleshoot refreshes and deployment issues, and support Git-based development and CI/CD practices. The role requires independent ownership of low- to moderate-complexity engineering work, technical documentation, and collaboration with senior engineers, DataOps, governance teams, consultants, and business stakeholders.

Build and maintain highly reliable data solutions supporting satellite design, manufacturing, and mission operations. Partner with engineering and business teams to understand challenges, design efficient data architectures, ensure availability and correctness, and use data to prioritize impactful solutions. Contribute to engineering excellence, transparency, collaboration, and continuous learning across the organization.

Build and operate production data pipelines, models, and data products supporting finance, accounting, treasury, compliance, and regulatory reporting. Partner cross-functionally to define requirements, model on-chain and internal data, and ensure data reliability through reconciliation, quality checks, monitoring, and alerting. Use SQL, Python, cloud data warehouse, Airflow, dbt, and AI tooling. Mentor engineers, review technical work, document systems, and communicate data concepts to technical and non-technical audiences.

Build and maintain production data pipelines, dbt models, Airflow workflows, and governed datasets supporting People Analytics. Integrate Workday and other HR systems into reliable Snowflake data products, implement data quality monitoring, row-level security, semantic models, and AI agents. Partner with engineers, analysts, data scientists, and business stakeholders to deliver trusted data solutions, communicate technical trade-offs, enforce governance, and mentor other engineers.

Build and maintain scalable, observable data pipelines using Apache Airflow and dbt. Develop SQL-based data models, testing, documentation, monitoring, and quality controls. Support analytics, forecasting, reporting, and enterprise integrations while improving pipeline reliability and performance. Collaborate with finance, marketing, operations, and other stakeholders to translate business needs into dependable data solutions.

Develop and maintain Databricks-based ETL/ELT pipelines using PySpark and SQL for marketing acquisition reporting. Migrate Snowflake transformations to Unity Catalog, ingest and transform vendor data, implement quality monitoring and alerts, troubleshoot failures, improve performance and reliability, and collaborate with analytics teams on reporting datasets.

Process and validate aircraft monitoring data, decode avionics bus data, troubleshoot C-17 systems, perform root cause analysis, develop data-mining algorithms, and provide technical recommendations to customers. The role supports program reviews, integrated product teams, design reviews, and aircraft health management for the C-17 fleet.

Leads a small data engineering team while contributing hands-on development. Responsibilities include designing and maintaining dbt and Python ETL pipelines, managing data quality and UAT, coordinating implementation timelines, conducting code reviews, mentoring engineers, documenting systems, and supporting project delivery. The role also involves building and deploying AI agents for data quality, pipeline monitoring, and analytics workflows, with a focus on healthcare claims data and large-scale datasets.

Develop and operate cloud-based data pipelines, dimensional data models, data warehouse solutions, CI/CD automation, and platform governance controls. The role integrates APIs and diverse data sources while supporting security, monitoring, incident response, and reliable data product delivery. Responsibilities include collaborating with product owners and engineering teams, participating in design and code reviews, troubleshooting technical issues, and applying repeatable DevOps practices.

Build and own AAG’s consolidated data warehouse, including production-grade infrastructure, ELT pipelines, dimensional models, and reporting layers. Integrate data from ERP, CRM, and manufacturing systems into Databricks on Azure, maintain reliable data flows, and support business decision-making across the organization. Collaborate with leadership to translate requirements into code, while managing system development, maintenance, documentation, and continuous improvement.

Designs and delivers large-scale, governed data pipelines and infrastructure across the full data lifecycle. Responsibilities include coding in Python and SQL, optimizing Airflow, dbt, AWS, Kafka, and Iceberg/Parquet workflows, implementing data quality and observability, contributing to architecture and tool evaluations, mentoring engineers, collaborating with stakeholders, and participating in on-call support.

Designs and leads manufacturing data architecture, repositories, models, integration patterns, pipelines, data services, and APIs. The role integrates complex data from equipment, sensors, quality, engineering, manufacturing, and enterprise systems; establishes standards and technical direction; and communicates risks and tradeoffs to senior stakeholders. Requires advanced SQL, programming, relational modeling, greenfield solution delivery, Secret or Top Secret clearance eligibility, flexible shift availability, and at least 10% travel.

Build and maintain scalable data pipelines, integrations, and data models connecting product, customer, sales, billing, and revenue data. Partner with Sales, RevOps, Finance, and Product to support forecasting, reporting, customer intelligence, segmentation, retention, and expansion analysis. Implement data validation, monitoring, documentation, and governance while improving infrastructure and automation for the company’s growing B2B operations.

Design, build, and maintain the company’s modern data warehouse and scalable ETL/ELT pipelines. The role focuses on warehouse architecture, data modeling, schema evolution, infrastructure selection, performance optimization, pipeline monitoring, data integrity, API integrations, and support for business intelligence analytics. The position is fully onsite in West Los Angeles and requires U.S. Person status.

Designs and maintains scalable data infrastructure, including ETL/ELT pipelines, data lakes, warehouses, validation processes, and governance controls. Collaborates with architects and business stakeholders to translate requirements into reliable data solutions. Uses Docker, Kubernetes, cloud platforms, Spark, and orchestration tools to deploy and automate data services. Supports AI and automation initiatives involving LLMs and agents while improving data quality, security, and accessibility.

Leads the strategy, architecture, and delivery of complex data platforms supporting investment research and portfolio construction. Designs Databricks and AWS lakehouse pipelines, data models, governance, testing, observability, and AI-ready metadata. Guides AI-agent engineering workflows, ensures security and reliability, manages cross-team initiatives, and leads technical reviews and engineer development. Partners with investment professionals and product managers to deliver trusted data products and scalable AI capabilities.

Design and modify data solutions using Databricks and Palantir Foundry; build data models and reports from ERP and legacy-system datasets; define and execute tests; and translate stakeholder reporting needs into technical specifications. The role requires SQL, Python, PySpark, database architecture, cloud architecture, software engineering practices, independent execution, documentation, and collaboration. It supports an Army client in a hybrid Arlington, Virginia environment and requires an active Secret clearance.

Build and maintain reliable data pipelines, computed tables, and scalable data models using SparkSQL and PySpark. Integrate IoT, product, customer, external, unstructured, and ML-related data into a central data lake for analytics, causal inference, model training, and dashboards. Ensure high data quality and uptime while collaborating with data science, analytics, AI/ML, and engineering teams.

Lead the reverse-engineering and migration of legacy ETL pipelines from SAP BusinessObjects Data Services and JD Edwards into dbt and Databricks architectures. Build automated data validation, maintain SQL Server write-back mechanisms, collaborate with client stakeholders, and lead and mentor a small engineering team. The role requires hands-on expertise in SQL, Python, Spark, orchestration, cloud platforms, and legacy ERP and ETL migrations.

Designs, builds, tests, and maintains scalable data infrastructure and ETL/ELT pipelines for mission partners. Uses AWS Glue, Glue Catalog, and Step Functions to integrate, migrate, transform, and validate data. Develops data models, schemas, queries, and production-ready datasets while collaborating with analytics and business teams. The role may also support AI/ML pipelines, data governance, DevOps workflows, Databricks, and real-time streaming architectures.

Architects, builds, tests, and maintains scalable data infrastructure and ELT/ETL pipelines using AWS Glue, Glue Catalog, and Step Functions. Performs data modeling, SQL querying, data migration, schema optimization, validation, and data quality improvements. Partners with analytics and business teams to transform raw data into production-ready datasets. The role may also support AI/ML pipelines, governance, access controls, DevOps workflows, and real-time data architectures.