Top Data Engineer Jobs in Sacramento, CA

As a Lead Data Engineer, you'll optimize data workflows, manage data engineering projects, mentor junior engineers, and ensure robust data solutions meet business needs.

Lead the design of DataOps platforms, focusing on scalable data infrastructures and automation frameworks. Oversee robust data orchestration and establish quality frameworks.

Design, build, and automate scalable batch and streaming data pipelines, implement data models and ETL, automate deployments, support ML/experimentation, maintain server architecture, and partner with stakeholders to deliver analytics and data-driven tools.

The Data Ingestion Specialist will manage the ingestion of healthcare data, improve data models, debug issues, and collaborate with team members to maintain pipeline reliability and efficiency.

As a Senior Data Engineer, you'll transform raw data into datasets, design production pipelines, integrate customer data, and collaborate with various teams to ensure data quality and reliability.

The Senior Data Engineer will design and implement lakehouse architectures, optimize ClickHouse deployments, develop ETL/ELT pipelines, and create data models to support analytical and operational functions.

Lead design and delivery of large-scale data pipelines and warehouse systems. Define technical strategy, enforce data governance, improve developer efficiency, mentor engineers, and align data engineering efforts with business goals to enable analytics and ML at scale.

Lead design and scaling of enterprise data architecture and pipelines. Build and maintain ETL/ELT and CDC/streaming workflows, improve data quality and observability, own schema and warehouse modeling, implement governance (RBAC), and provide cross-functional technical leadership to enable trusted, scalable data for analytics and product teams.

The Senior Data Engineer will design and build data pipelines and infrastructure to support data science and business intelligence solutions, utilizing AWS services and streaming technologies.

Remote entry-level Big Data Engineer role building data acquisition and munging pipelines. Requires strong Python3 (NumPy, Pandas), advanced SQL, scalable algorithm and data-structure implementation on distributed clusters, converting code to functional paradigms, and the ability to learn PySpark or Kafka quickly. Product-development, startup experience and real-world pipeline projects preferred.

Build, maintain, and optimize cloud data pipelines and dbt models; design Airflow DAGs; implement dimensional models and BI dashboards; integrate healthcare data with HIPAA-compliant practices; contribute to RAG pipelines and MLOps workflows; review contractor code, monitor data quality, and document designs following governance standards.

The Data Integration Engineer will manage data integrations and ETL processes for healthcare data, ensuring accuracy and accessibility for business users.

As a Senior Data Engineer, you will design and optimize data pipelines, enhance system efficiency, and collaborate with teams to drive health outcomes using large healthcare datasets.

As a Senior Data Engineer, you will enhance the data lakehouse, optimize data workloads, perform complex transformations, and develop frameworks to improve delivery velocity, focusing on cybersecurity solutions.

Design, build, and scale reliable batch and real-time data pipelines and warehouses. Partner with engineering, data science, and business teams to define data models, improve observability, optimize storage and querying, and evangelize data engineering best practices across the company.

Design, build, and maintain scalable, fault-tolerant streaming data architectures and real-time pipelines using Kafka, MSK, and Kinesis. Implement stream processing, monitoring, security, IaC, and CI/CD. Collaborate with data teams, provide technical leadership, and optimize latency, throughput, and cost for real-time analytics and downstream systems.

Lead data engineering for a healthcare scheduling platform rebuild: map legacy multi-TB schema, design target schema, build translation layer (views/materialized views and write mappers), ensure data consistency during parallel systems, plan per-tenant cutover with verification and rollback, tune DB performance, instrument data quality/lineage/audit, and mentor engineers.

Build and maintain a near real-time financial dashboard by integrating market and forecast APIs, automating financial models, and crafting clear visual narratives using Flourish and Canva. Collaborate with design and marketing partners, ensure data accuracy and performance, and optimize data flows across multiple feeds.

Design and build scalable batch and streaming data pipelines, lead architecture for Lakehouse platforms, and mentor junior engineers while optimizing data quality and performance.

Design, build, and maintain scalable Databricks-based data pipelines and ELT workflows; develop and document dbt models; define data modeling methodology; ensure data quality, reliability, and scalability; collaborate with clients and cross-functional teams; provide technical guidance and deliver improvements to clients' data infrastructure and analytics capabilities.

As a Senior Data Engineer, you will manage data pipelines, ensure data quality, partner with teams for data-driven decisions, and improve the data platform while maintaining data integrity.

The Senior Data Engineer will create and manage data pipelines, ensuring data quality, collaborating with teams for data-driven decisions, and evolving the data platform.

The Senior Data Analytics Engineer analyzes and delivers business intelligence solutions, builds data pipelines, and collaborates with clients to meet their data-driven needs.

The Senior/Staff Data Engineer will design and scale the core data platform for healthcare systems, focusing on infrastructure, tooling, and governance while mentoring engineers and collaborating with various teams.

The Senior Data Engineer will design and build scalable data platforms, handle automation for data operations, and ensure reliability and performance of data pipelines while collaborating with various teams.