Top Data Engineer Jobs in Los Angeles, CA

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 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.

Architects, deploys, and operates large-scale Apache Kafka and Confluent streaming platforms. Responsibilities include cluster sizing, configuration, security, automation, observability, HA/DR, troubleshooting, and developer enablement. Partners with application and cross-functional teams to deliver reliable event-driven solutions, translates ambiguous requirements into production-ready designs, contributes to code and design reviews, and mentors junior engineers.

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.

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.

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.

Own Eve’s data platform end to end, including ingestion, orchestration, incremental dbt models, Snowflake administration, Terraform infrastructure, observability, access controls, and pipeline reliability. Build schema-change detection, freshness SLAs, CI environments, and incident processes while managing performance and compute costs. Partner with analytics engineers and stakeholders to maintain trusted data for reporting and AI systems, using AI-assisted development and documenting platform standards.

Leads the design, development, and operation of Astrana’s enterprise data platform. Builds scalable Databricks, Spark, PySpark, SQL, and Delta Lake pipelines; integrates healthcare data; develops unified data models and APIs; supports Lakebase and PostgreSQL applications; and implements governance, data quality, monitoring, CI/CD, automation, and performance optimization. The role also handles incidents, disaster recovery coordination, engineering standards, code reviews, and mentoring across internal and vendor teams.

Leads the architecture, development, and production operation of complex data pipelines and platforms. Resolves scalability, data quality, reliability, and architectural challenges; evaluates data technologies; mentors engineers; establishes engineering standards; partners with product, analytics, and operations teams; communicates technical trade-offs; and conducts proofs of concept to guide platform strategy.

Architects and develops enterprise-scale data platforms, schemas, models, pipelines, and integration solutions. Builds cloud-native data solutions, ETL/ELT frameworks, API integrations, and automated ingestion processes. Leads SQL development, database performance tuning, query optimization, data governance, and business intelligence enablement. The role requires expertise in Power BI, DAX, Power Query, data modeling, and multiple BI tools, with preferred knowledge of disputes, chargebacks, fraud operations, and case-management systems.

Build and maintain scalable Snowflake-based ELT/ETL pipelines, production data models, and observability frameworks. Translate business requirements into reliable solutions supporting analytics, BI, and ML. Establish data quality, governance, lineage, security, and compliance practices while optimizing warehouse performance, cost, and data freshness. Collaborate with Analytics, Product, and Engineering teams, contribute to architecture decisions, evaluate Snowflake tooling, and use AI-assisted development tools to accelerate delivery.

Designs, builds, and optimizes scalable data collection, storage, access, and analytics systems. Creates and automates reliable data pipelines, supports data quality and governance, ensures SLA and performance compliance, and monitors pipeline operations. The role requires collaboration with analytics, business, and customer teams, with potential work involving AI/ML pipelines, Databricks, DevOps practices, streaming data, and event-driven architectures.

Build and maintain scalable batch and incremental data pipelines using Python, SQL, and Snowflake. Develop data warehouse models, transformations, automation, quality checks, lineage, alerting, and performance optimizations. Collaborate with analysts, data scientists, and application teams to deliver trusted datasets, while documenting pipelines, schemas, and data contracts.

Build and maintain scalable batch and incremental data pipelines using Python, SQL, and Snowflake. Develop warehouse models, transformations, automation, data quality checks, lineage, alerting, and performance optimizations. Collaborate with analysts, data scientists, and application teams to deliver trusted datasets, while documenting pipelines, schemas, and data contracts.

Designs and maintains a scalable data platform supporting analytics, machine learning, and AI workloads. Builds tooling for reliable data pipelines, semantic layers, data agents, LLM applications, and evaluation systems. Ensures data quality, availability, observability, and reliability while establishing modeling, testing, and platform best practices. Collaborates with Data Science and Product Engineering, leads solutions from design through implementation, and contributes individually in a fast-paced startup environment.

Build, test, and maintain batch and event-driven data pipelines and ETL/ELT workflows. Transform and validate large datasets, troubleshoot data-quality issues, improve workflow reliability, and support platform modernization. Collaborate with engineering, product, and data stakeholders while following strong practices for version control, testing, monitoring, documentation, and code review. The role may occasionally require work outside standard business hours.

Design, build, and optimize scalable data pipelines using Databricks, PySpark, SQL, Delta Lake, and lakehouse architecture. Collaborate with analytics, product, and engineering teams while developing batch and streaming workflows, integrating data warehouse systems, and using cloud platforms and orchestration tools such as Azure, Airflow, ADF, or Prefect.

Own EULER’s data lifecycle, including instrumentation, validation, reconciliation, privacy, anonymization, canonical data modeling, analytics datasets, documentation, and production pipelines. The role requires strong SQL, software engineering practices, independent ownership, skepticism toward data quality, and clear communication. Preferred experience includes GDPR/LGPD, multi-tenant B2B SaaS, dbt, statistics, Cloudflare Workers, Postgres, Parquet, DuckDB, and Bubble.

Develop and maintain scalable data pipelines, ETL/ELT processes, APIs, data models, and analytics datasets using Python, SQL, Spark, and Databricks. Integrate sensitive federal health, financial, and operational data while implementing validation, governance, testing, documentation, and performance optimization. Collaborate with engineers, architects, analysts, and stakeholders to deliver reliable data products and resolve technical data needs.

Build and maintain scalable batch and streaming data pipelines, analytics warehouses, transformation layers, and dimensional data models. Perform SQL-based analysis, support dashboards and reporting, monitor data quality and delivery SLAs, troubleshoot incidents, and improve operational reliability. Collaborate with engineers, analysts, data scientists, and business stakeholders while contributing to documentation, governance, observability, and automation.

Build scalable data ingestion, transformation, and loading infrastructure using Spark, SQL, AWS, and Databricks. Develop entity resolution systems, CI/CD pipelines, anomaly detection, and data quality processes. Design scalable processing systems, orchestration workflows, storage patterns, and data warehouse solutions. Collaborate remotely, document technical designs, scope projects, and solve complex data engineering and data science problems.

Build and scale distributed data pipelines and architectures supporting AI products and research. Responsibilities include processing large datasets with Spark, designing AWS data systems, managing SQL and NoSQL storage, writing Python and SQL, ensuring data quality and reliability, and implementing automation, orchestration, monitoring, and scalable data operations.

Design, build, and operate scalable batch and streaming data pipelines, lakehouse and warehouse models, and data services. Own datasets end to end, improve Snowflake, Iceberg, Spark, and Flink performance and reliability, implement governance and observability, and support graph-serving data models. Partner with product and engineering teams, participate in on-call, review code and designs, and mentor junior engineers.