Top Data Engineer Jobs

Lead a team to define architecture and roadmap for enterprise data lake and analytics platforms. Design and deliver large-scale batch and real-time pipelines, feature stores, metadata/lineage, and automated data quality frameworks to support production ML, advanced analytics and GenAI. Collaborate with data scientists, product and enterprise architects to evaluate emerging data technologies.

Design and implement enterprise data architecture and pipelines, build data models and metadata standards, collaborate with stakeholders to meet data requirements, lead technology assessments and digital ecosystem recommendations, ensure secure data storage and retrieval, and support data governance, quality, and analytics delivery.

Design, implement, and maintain scalable big-data pipelines and ETL on cloud (GCP). Build production data solutions using Python, Spark, Scala, Airflow, Kafka, and BigQuery; collaborate with data scientists and product, estimate and scope projects, drive data engineering best practices, and mentor teammates.

Responsible for designing, developing, and maintaining scalable data pipelines and infrastructure, implementing data governance, and collaborating with teams to meet data needs.

Build and operate the end-to-end data layer: ingest and sync EHRs and customer systems, transform messy sources into a normalized patient/provider model, maintain data freshness and query performance, and enable natural-language query interfaces and serving for production AI workloads.

Lead design and implementation of a secure, decentralized data sharing framework and resilient patterns (e.g., CQRS) for distributed microservices. Align storage strategies across relational, indexing, and time-series engines, maintain multi-domain data models and contracts, collaborate with backend, infrastructure, and platform teams to scale and secure data systems across bare-metal and cloud environments.

Build and own end-to-end data infrastructure: ingest and sync EHRs and other customer systems, transform messy sources into a normalized patient/profile model, maintain pipeline performance and freshness, optimize queries at scale, and enable natural-language query tooling over healthcare data.

Design and maintain ingestion pipelines for 835/837 and scanned EOBs: OCR, LLM-assisted extraction, integrations (SFTP, clearinghouses), data modeling, validation, and reconciliation to produce deterministic, trustworthy healthcare records.

The Senior Data Engineer designs, builds, and maintains data pipelines and ensures data quality controls while supporting data analytics and reporting needs.

Seeking a Senior BODS Developer to develop and maintain complex BODS processes for data transformation. The role includes improving data quality using Information Steward and GAC/URAC techniques, collaborating with teams, creating documentation, and mentoring others.

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.

Design, build, and optimize scalable data infrastructure and pipelines on AWS using Spark/Scala. Store and model data in appropriate engines and formats, implement knowledge graphs accessible via batch jobs and APIs, enforce automated data quality checks, and collaborate with Data Science and Product teams to deliver performant, cost-effective data services.

Design and implement scalable data infrastructure and pipelines on AWS using Spark and Scala. Build fault-tolerant batch and streaming workflows, optimize storage formats and engines, enforce automated data quality checks, and collaborate with Data Science and Product teams to deliver reliable data assets and APIs.

The Data Platform Engineer will support Corporate Data Science and Analytics teams by curating datasets, optimizing code, and building ETL flows for data products. Responsibilities include managing production services and using cloud technologies.

Design and build scalable, fault-tolerant batch and streaming data pipelines and data models for Roku devices and clients. Maintain petabyte-scale data warehouse, ensure data quality, optimize performance, debug large clusters, and collaborate across teams to deliver trusted analytics products and influence roadmap.

Design and build scalable batch and streaming data pipelines and data models for Roku devices and clients. Own data mapping, transformations, quality standards, debugging, and performance tuning on petabyte-scale warehouses. Influence architecture and product roadmap, support existing platforms, and deliver trusted data products enabling analytics and product decisions.

Design, build, and maintain scalable data pipelines and tenant-aware APIs to ingest and transform data from varied sources. Work with event-driven and batch architectures, implement authentication and integration patterns, ensure observability/monitoring, optimize data flows for cost and performance, and collaborate with product, infrastructure, and engineering teams to onboard and maintain data sources at scale.

As a Data Platform Engineer, you'll design and maintain scalable data systems, partner with teams on product direction, and ensure system reliability and security.

The Senior Data Engineer will design and implement complex data models, collaborate on data governance, lead data initiatives, and mentor team members.

Lead engineering strategy and hands-on implementation for Finance & Actuarial data platforms. Architect no-touch automated data pipelines, drive cloud migrations (Azure Synapse/Databricks/Snowflake), embed AI-assisted development and modern DevOps, establish CI/CD and runbooks, resolve complex incidents, evaluate tooling, and mentor engineering squads while communicating strategy to executive stakeholders.

As a Staff Data Engineer, you'll oversee Gamma's data infrastructure, architect scalable systems, mentor engineers, and drive strategic decisions for data usage across the organization.

Lead architecture and implementation of an AWS-native data platform powering analytics, ML, and AI workflows. Build scalable ETL/ELT pipelines (Airflow, Fivetran, dbt), enforce governance, observability (Datadog), and data quality, and provide technical leadership across Data Science, Analytics, Product, and Engineering.

Lead and mentor multiple Databricks-focused data engineering teams, define engineering best practices and reusable frameworks, drive CI/CD and performance/cost optimization, build and support analytical and streaming pipelines, and collaborate with architects, product owners, and cross-functional partners to deliver high-quality data products.

Lead design and delivery of Databricks-based Lakehouse solutions and end-to-end data engineering projects. Build and optimize ETL, streaming and batch pipelines, integrate cloud and on-prem systems, mentor team members, support pre-sales, and ensure governance, security, and platform performance.

Design, build, and maintain data models, ETL pipelines, and SQL-based solutions to support reporting and analytics. Analyze source systems for data quality, develop data integration using Databricks/Informatica/Pentaho, and produce documentation, dashboards, and BI artifacts (Power BI/OBIEE/Tableau) to support DSNY operations.