Top Data Engineer Jobs in Los Angeles, CA

Design and maintain data architecture and pipelines to support compliance and risk teams. Build and optimize data models, standardize metrics, and create data dictionaries. Implement data quality, lineage monitoring, AI-driven agents for false-positive reduction and automation, and participate in on-call rotations to ensure SLAs are met.

Designs and leads scalable data pipelines, ETL processes, data models, analytics products, dashboards, and data quality frameworks. Partners with data scientists, product, security, and compliance teams to integrate production AI/ML solutions, establish governance, and support responsible AI practices. Provides technical leadership through architecture, design reviews, mentoring, roadmap alignment, and evaluation of emerging data technologies.

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.

Build and operate a Kubernetes-based data platform spanning lakehouse storage, ingestion, transformation, entity resolution, lineage, and data quality. Develop batch and streaming pipelines across heterogeneous and disconnected environments, create full-stack tools for subject matter experts, and expose documented APIs for applications and AI agents. Collaborate with product, AI/ML, infrastructure, mission partners, and field teams while mentoring engineers and supporting secure, air-gapped deployments.

Administer and enhance Edmunds’ Databricks data platform and AWS infrastructure. Build and maintain ETL pipelines, infrastructure-as-code tooling using Terraform or CDK, and operational dashboards, alerts, and reports. Collaborate with business, engineering, analytics, security, and infrastructure teams to support data platform users and AI solutions. Evaluate new technologies, troubleshoot platform issues, and improve operational, cost, and security visibility.

Own and evolve UJET’s data platform, including MySQL CDC replication to BigQuery, dbt transformations, Terraform infrastructure, pipelines, data quality, observability, and Looker models. Build reliable, analytics-ready datasets and trusted metrics for internal and customer-facing reporting. Partner with Analytics, Finance, Product, and Engineering to deliver scalable data solutions, improve platform reliability, optimize performance and costs, and establish development standards and guardrails.

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.

Lead the technical direction and hands-on development of scalable data pipelines, data-serving platforms, and AI-enabled data frameworks. Design systems across ingestion, transformation, serving, and consumption; improve data quality, observability, reliability, security, query performance, and infrastructure efficiency. Lead cross-team initiatives, resolve complex production issues, mentor engineers, guide architectural decisions, and apply AI effectively throughout software development.

Designs and manages enterprise Databricks data platforms, including workspaces, clusters, Unity Catalog, SQL warehouses, Terraform infrastructure, and secure data ingestion pipelines. Builds medallion architecture with Delta Lake, supports Genie dashboards and self-service analytics, implements governance and access controls, and maintains CI/CD, testing, documentation, and operational processes. Partners with technical and business stakeholders and supports MCP connectors and agentic data integrations.

Own and evolve WorkOS’s internal data platform, including ingestion, orchestration, Snowflake, dbt models, data governance, reverse ETL, semantic views, and AI-enabled data workflows. Ensure pipeline reliability, freshness, security, and scalability while partnering with Product, Finance, RevOps, GTM, Engineering, and Security. Build monitoring, runbooks, CI/CD gates, access controls, masking policies, and infrastructure automation across AWS and Kubernetes.

Lead pre-sales technical engagements to design and optimize scalable cloud lakehouse data platforms (Snowflake/Databricks). Provide architecture reviews, ETL/ELT pipeline design, governance and AI-ready data foundations, support services sales, and translate technical tradeoffs into actionable recommendations for engineering and executive stakeholders.

Build and optimize batch and streaming data pipelines, ETL/ELT processes, data models, and analytics solutions on Google Cloud. Support Vertex AI and Gemini-based ML solutions, MLOps, prompt engineering, embeddings, and RAG experimentation. Participate in client requirements gathering, architecture implementation, data governance, quality validation, security, monitoring, and documentation while collaborating with senior engineers and architects.

Build and maintain data pipelines, ingestion workflows, transformations, and data products using Python, SQL, and cloud data platforms. Test and troubleshoot systems while applying Git, software testing, and CI/CD practices. Collaborate with senior engineers and cross-functional stakeholders, use AI-assisted tools, and deliver reliable data solutions in a fast-paced startup environment.

Builds scalable ELT pipelines, data models, business metrics, and semantic layers supporting analytics, reporting, and AI/ML applications. The role ingests data into Snowflake, develops dbt and SQL transformations, validates data quality and governance, and partners with product managers, analysts, data scientists, visualization teams, and business stakeholders. Responsibilities include harmonizing media data, delivering ready-to-use datasets, and supporting strategic decision-making across television production, distribution, and marketing.

Lead architecture, development, and optimization of a cloud-native data platform. Design high-volume batch and streaming pipelines, optimize data processing across AWS, Snowflake, Redshift, and Athena, and implement Iceberg-based data management. Establish security, privacy, observability, and DataOps practices while mentoring engineers, guiding technical direction, collaborating with data science teams, and delivering scalable petabyte-level data infrastructure.

Design, build, deploy, and maintain AWS-based data and AI/ML infrastructure for enterprise agentic AI platforms. Develop data lake pipelines, observability systems, agent-enablement tooling, and production MLOps/LLMOps capabilities. Own features end-to-end, contribute to architecture and design decisions, optimize reliability, scalability, security, and cost, and collaborate with AWS Professional Services, client teams, and junior engineers.

Develop and maintain end-to-end data pipelines, data models, backend architectures, and campaign-planning tools for Disney’s Performance Marketing team. Establish engineering standards for pipeline development, observability, documentation, quality controls, and monitoring. Collaborate with marketers, data scientists, analysts, and engineers to turn prototypes into reliable production applications and ensure advertising optimizations and business metrics remain accurate.

Design, build, and lead scalable data platforms, pipelines, data products, and ELT/ETL processes supporting analytics and AI/ML workloads. Establish standards for data quality, testing, monitoring, observability, CI/CD, security, and reliability. Own projects through deployment and production support, collaborate across technical and business teams, use AI-assisted engineering tools, and mentor data engineers.

Design and build scalable data pipelines, data-serving platforms, and AI foundations for financial data. Ensure data integrity and quality, optimize queries, troubleshoot data issues, and promote AI-assisted software development. The role requires strong software engineering, batch processing, cloud infrastructure, database, and data warehouse experience, along with effective communication and creative problem-solving.

Designs and maintains scalable data pipelines, ETL processes, data models, data quality frameworks, and analytics tools. Collaborates with data scientists, product managers, and stakeholders to translate requirements into technical solutions. Develops reusable analytics libraries, visualizations, dashboards, and querying tools while ensuring data governance and security compliance. Performs exploratory and applied analytics to identify trends, anomalies, and actionable insights.

Build and operate production pipelines for legal records from PACER, court systems, opinions, PDFs, scans, XML, and HTML. Develop LLM-assisted extraction workflows, validate data quality, optimize SQL, and support RAG and semantic search products. Manage AWS S3 and RDS systems within security guardrails while collaborating with engineering teams. The role requires strong Python and SQL skills, legal data experience, document extraction expertise, and autonomous problem-solving with large, messy datasets.

Lead enterprise data engineering efforts supporting defense missions. Responsibilities include prioritizing technical tasks, mentoring engineers, conducting code reviews, translating government requirements into scalable data architectures, developing Databricks ETL/ELT pipelines with Python, PySpark, and SQL, troubleshooting pipeline performance and reliability, and documenting systems. The role collaborates with government stakeholders, product owners, data scientists, analysts, and technical teams to deliver secure, high-throughput data platforms and actionable insights.

Designs and automates data validation, profiling, testing, monitoring, and quality frameworks for multi-source cloud data. The role uses Azure Synapse, Databricks, SQL, Python, Scala, Collibra, and Profisee to detect anomalies, enforce governance, and improve data reliability. Responsibilities include developing automated test plans, analyzing structured and unstructured datasets, supporting Azure data engineering workflows, and collaborating with engineers, analysts, data scientists, and product teams. A Public Trust or Suitability/Fitness determination may be required.

Designs and maintains scalable Azure data pipelines and ETL/ELT solutions across data lakes, warehouses, lakehouses, streaming platforms, and enterprise sources. Builds integrations using Azure Data Factory, Synapse, Databricks, Spark, Kafka, and related services; applies security, governance, monitoring, and performance optimization practices. Develops Power BI dashboards, reports, data models, and visualizations, while supporting analytics and AI/ML use cases. Collaborates in Agile teams and automates deployments through DevOps and CI/CD practices.

Designs, builds, documents, tests, and maintains large-scale data pipelines across platforms and data sources. Performs ETL and ELT, develops data quality and governance processes, and supports data security and compliance. Collaborates with architects, analysts, and stakeholders to translate business needs into technical solutions. The role also provides mentorship to junior engineers and leads data collection, storage, and process automation initiatives.