Top Entry Level Data Engineer Jobs in San Francisco Bay Area, CA

Build and maintain scalable data pipelines, analytical data models, backend data systems, business rules, metrics, and data sources. Partner with teams across finance, growth, product, customer support, sales, and marketing to support analytical needs and improve data-driven decision-making. Own data engineering problems end to end and help advance organizational data practices.

Build and maintain Nexus, an enterprise AI implementation platform. Develop backend services, APIs, integrations, orchestration workflows, automated data pipelines, transformations, semantic models, validation processes, and governed lakehouse assets. Partner with AI engineers and enterprise customers to deploy solutions, resolve complex data and system requirements, and create reusable integrations. Ensure secure, monitored, recoverable, and repeatable deployments across customer environments.

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.

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.

Builds and maintains data pipelines and datasets supporting analytics, AI, and modeling. Responsibilities include ingesting healthcare claims and lab data into Snowflake and AWS Iceberg, processing structured and unstructured sources, curating data for AI use cases, implementing medallion architectures, evaluating data quality, and contributing to pilot initiatives using SQL, Python, AWS Glue, Terraform, and Snowflake.

Build and maintain FinOps data pipelines, BigQuery and dbt models, cost attribution systems, forecasting and anomaly detection models, and cost-versus-revenue analytics. Identify and implement AWS and GCP savings opportunities, automate governance with Python, Terraform, and Lambda, and partner with Engineering, Finance, Product, and leadership on cloud costs, forecasting, reporting, and architecture decisions.

Build and scale foundational data infrastructure for AI-powered hardware and consumer devices. Responsibilities include developing reliable pipelines, integrating manufacturing, telemetry, quality, and customer operations data, creating durable data models, enabling manufacturing and product intelligence, and establishing data engineering standards. The role requires strong architectural judgment, distributed systems knowledge, production infrastructure experience, and cross-functional collaboration in an early-stage organization.

Design, build, and maintain scalable big data pipelines, architectures, distributed databases, and storage systems. Use Python and frameworks such as Hadoop, Spark, or Flink for data integration, transformation, and processing. Ensure system performance, availability, security, governance, and data quality while collaborating with cross-functional teams and documenting technical solutions.

Own Modal’s go-to-market data platform, including organization and people graphs, identity resolution, CRM schemas, Snowflake and dbt models, reverse ETL pipelines, revenue operations metrics, forecasting, attribution, and data quality systems. Partner with Data, RevOps, and Marketing to support customer context, routing, territory management, campaigns, reporting, and reliable GTM workflows.

Build and maintain data pipelines for network and operational telemetry, integrate DataMiner and other monitoring systems, create Power BI dashboards, configure monitoring metrics and alerts, automate reporting, analyze network performance and recovery events, and translate technical findings into actionable insights for technical and executive stakeholders.

Build and operate data pipelines that integrate customer financial data from ERPs, warehouses, and payment systems. Develop AI agents that automate ERP implementations, partner with implementation teams to understand and transform customer data, and contribute to shared AI platform capabilities. The role requires strong full-stack engineering fundamentals, autonomous execution, and comfort working in an ambiguous, fast-moving environment.

Build and maintain production data ingestion pipelines, transformation models, warehouses, and schemas for PerfectServe’s data platform. Deploy reliable, tested pipelines using idempotency and data-quality checks, apply dimensional and medallion modeling, participate in design reviews, and document owned pipelines and runbooks. The role requires SQL, programming experience, dbt or similar transformation tools, cloud data warehouse experience, ELT orchestration, AWS knowledge, and a strong data-testing mindset.

Lead the design and development of Azure-based big data solutions, including batch and real-time ETL/ELT pipelines, data architecture, ingestion, curation, warehousing, and storage. Build solutions using Azure Data Factory, Databricks, Synapse, Spark, Python, SQL, and related Azure services. Support Salesforce migration and integration while applying strong RDBMS, data management, and big data design expertise.

Build and maintain Google Cloud data pipelines and ETL/ELT processes supporting GenAI and RAG solutions. Classify and govern sensitive data using Dataplex and Cloud DLP, enforce access controls, assess AI readiness, and design reusable schemas and data models. Collaborate with engineering, delivery, and client teams to integrate legacy systems, monitor pipeline reliability, conduct data quality testing, document data flows, and proactively identify compliance and delivery risks.

Build and maintain full-stack data engineering solutions supporting BioIntelliSense’s remote patient monitoring and clinical intelligence platform. The role likely involves processing health-monitoring data, enabling analytics, and supporting reporting for clinicians, patients, and employers. Specific responsibilities and qualifications are not provided in the description.

Design and deliver cloud-native data platforms, scalable big data pipelines, lakehouses, warehouses, transformation frameworks, BI solutions, and ML-enabled data workflows. Lead client-facing pre-sales engagements, technical workshops, proof-of-concepts, executive conversations, and enterprise data modernization initiatives. Build reusable architectural IP, influence senior stakeholders, and translate ambiguous business challenges into production-ready solutions using AWS and modern data technologies.

Own Mercor’s data platform infrastructure across Snowflake, Fivetran, Airflow, Astronomer, and Hex. Responsibilities include data organization, role-based access control, row-level security, PII masking, governed migrations, pipeline reliability, on-call support, dashboard governance, warehouse cost optimization, and CDC or streaming ingestion. The role also establishes platform standards and builds internal tooling for a new Data Platform team.

The role involves structuring and ingesting customer data, setting up ETL pipelines, and providing solutions for data export and import.

Build and maintain production-grade dbt data models and pipelines across ingestion, transformation, and visualization. Develop reliable datasets supporting reporting, analysis, finance, revenue recognition, and company decision-making. Collaborate with Finance, Engineering, Sales, Product, and Marketing to understand data needs, improve manual workflows, validate data accuracy, and create scalable solutions using modern data tools.

The Data Engineer will build and scale Baseten's internal data platform, designing data models and pipelines while enabling self-service analytics and ensuring data reliability.

The DMS Data Engineer is responsible for analyzing raw data, building data systems and pipelines, conducting complex data analyses, and collaborating on projects to enhance data quality and reliability.

The job involves leveraging AWS and Snowflake for data engineering tasks within a technology-focused company, requiring US residency.

Develop ETL and data visualization solutions for a big data customer and partner experience transformation program. Build native Hadoop and Vertica ETL pipelines, collaborate with stakeholders, and support data integration and visualization deliverables for analytics teams.