Data Engineer Jobs in Berkeley, CA

Lead design and implementation of enterprise data architecture and cloud-based data solutions. Manage large data engineering projects, develop data models and pipelines, ensure governance and security compliance, advise clients strategically, and build high-performing inclusive teams.

Build and operate production machine learning systems for ranking, retrieval, recommendations, search, propensity, churn, LTV, and next-best-action decisioning. Design reliable signal contracts covering freshness, provenance, confidence, eligibility, and calibration. Lead feature pipelines, model serving, experimentation, monitoring, and feedback loops while evaluating fairness, risk, compliance, trust, and long-term customer impact. Collaborate across product, growth, data, platform, modeling, risk, and compliance teams.

Build and operate production machine learning systems for ranking, retrieval, recommendations, search, propensity, churn, lifecycle intelligence, and next-best-action decisioning. Design reliable signal contracts with freshness, provenance, confidence, and calibration guarantees. Lead experimentation, monitoring, feedback loops, and impact evaluation focused on fairness, trust, risk, compliance, and long-term engagement. Partner across product, growth, data, platform, modeling, risk, and compliance teams.

Own Orum’s data platform architecture, roadmap, reliability, and scalability. Build batch and real-time ETL/ELT pipelines using PostgreSQL, event streams, ClickHouse, BigQuery, and dbt. Administer Looker and develop LookML models, dashboards, and a trusted semantic layer. Establish data quality, governance, lineage, observability, and metric standards while partnering with Product, Finance, GTM, and Operations on analytics, AI, and business intelligence initiatives.

Build and operate Block’s data platform, including warehousing, orchestration, business intelligence, governance, and agentic workflows. Develop reliable Python services, developer tools, and Terraform infrastructure for petabyte-scale data. Partner with data practitioners and product teams, establish engineering standards, provide technical direction, and mentor engineers while ensuring security, reliability, observability, usability, and data governance.

Build and operate a scalable data platform supporting warehousing, orchestration, business intelligence, governance, and agentic workflows. Develop Python services, Terraform infrastructure, and developer tools for petabyte-scale data. Partner with data practitioners and product teams, establish security and reliability patterns, set technical direction, improve engineering standards, and mentor engineers.

Build and operate high-volume telemetry and analytics data pipelines supporting real-time monitoring, product decisions, experimentation, and user experience analysis. Design event instrumentation, schemas, quality checks, deduplication, late-event handling, and identity stitching. Partner with data science, product, and SRE teams to shape requirements, and integrate AI tooling into development and data quality workflows.

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 maintain scalable, reliable ML data platform systems to support dataset generation, model training, analytics, monitoring, and large-scale inference. Collaborate with ML, software, and infrastructure engineers to design cost-efficient data lake and training infrastructure using vendor and open-source tools to enable next-generation ML models.

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.

The AI-Native Data Engineer will build data pipelines, architect databases, design GCP infrastructure, and automate workflows for energy management using AI.

The role involves designing and building data pipelines for analyzing user feedback on AI models, ensuring data quality and infrastructure scalability.

Design and implement secure, resumable cloud pipelines to ingest multi-terabyte medical imaging data; build GPU-accelerated orchestration with strong retry semantics and cost controls; define data lifecycle and retention; implement HIPAA/PHI security and compliance controls; and deliver operational tooling (monitoring, alerting, runbooks) for a device fleet.

Build and operate streaming and batch data pipelines for energy-storage telemetry. Design reliable, query-ready time-series and event datasets using open table formats, orchestration frameworks, AWS, and infrastructure-as-code. Lead technical designs, partner with field operations and engineering teams, support on-call operations, and mentor engineers while improving data reliability and engineering practices.

Build and maintain scalable financial data infrastructure, including ingestion, validation, normalization, and integrations. Develop systems that calculate portfolio performance metrics efficiently across high-volume datasets, improve reliability and observability, and support distributed processing. Create internal tools, dashboards, reusable schemas, asset data feeds, and self-service analytics capabilities. Collaborate cross-functionally to expand market-data coverage, support B2B integrations, and strengthen operational resilience.

Lead architecture and implementation of fleet telemetry, time-series storage, streaming pipelines, and CRM/warehouse integrations on AWS. Build tools for proactive fleet health, partner cross-functionally (firmware, manufacturing, ops), support on-call as systems mature, and design LLM-driven automations to increase team leverage.

Build and operate scalable data ingestion, transformation, and connector frameworks; design and maintain a knowledge-graph-based data fabric; normalize and vectorize enterprise data for LLM/AI workflows; implement governance, lineage, access controls, and secure APIs to serve ML/agent pipelines.

Build and scale AI data and ML infrastructure: evaluation systems, fine-tuning pipelines, agent-first product surfaces, high-throughput data workflows, and integrations of new models into production.

Lead architecture and implementation of agentic AI systems powering Zuma's platform. Rebuild onboarding and integration frameworks, implement analytics and observability for LLM performance, translate product needs into technical solutions, mentor engineers, establish engineering standards, and work directly with customers to enable scalable, production-grade AI experiences.

Design and implement real-time MAC data-plane software for high-throughput satellite links. Build packet-processing pipelines, queue and buffer management, retransmission and loss recovery, and integrate with PHY, modem, FPGA, DMA, and SoC hardware. Optimize for low latency and high throughput using zero-copy, scatter-gather, lock-free structures, and hardware offload. Lead architecture through prototyping, hardware integration, performance qualification, and operational deployment while diagnosing timing and synchronization issues.

Responsible for utilizing Hortonworks architecture and tools such as Hadoop and Spark for data analysis and engineering tasks. Candidates should have relevant experience and may be considered for contract-to-hire roles.

Lead strategy and operations for on-prem Kafka and support Perforce-based asset version control. Maintain mission-critical Linux services, automate with scripting and IaC, troubleshoot complex issues, coordinate with production/engineering/vendors, mentor teammates, and participate in on-call rotations.

Seeking Data Platform Engineers to design, build, and manage scalable data pipelines and analytic query engines while ensuring data integrity and performance optimization.

Own and improve annotation systems, workflows, and tooling to produce high-quality training data. Build Python and web tools, integrate model-assisted labeling and active learning, implement data quality checks, support dataset versioning and evaluation, and coordinate cross-team efforts to measure and improve annotation throughput and model impact.

As a founding Data Scientist/Machine Learning Engineer, you'll develop AI/ML models, enhance product capability, and drive impactful user outcomes while working closely with product teams.