Top Data Engineer Jobs

Design, build, and operate scalable Databricks data pipelines and lakehouse solutions for observability, infrastructure, and security data. Develop PySpark and Structured Streaming workloads, optimize performance, establish data quality and governance standards, and support reliable enterprise data products. Collaborate with security, infrastructure, observability, and platform teams on data source onboarding, CI/CD, monitoring, access controls, and platform optimization.

Designs, builds, optimizes, and maintains data pipelines, ETL/ELT workflows, data platforms, and data models. Supports high-volume data processing, cloud and hybrid modernization, data quality, metadata, lineage, governance, and analytics-ready datasets for federal mission and AI/ML applications. Collaborates with data scientists, architects, and analysts while producing technical documentation. The role may be filled across junior, mid-level, senior, or principal career levels.

Build and operate scalable cloud data pipelines, warehouses, transformation layers, and ML production systems. Lead data infrastructure architecture, warehouse performance optimization, DataOps monitoring, CI/CD improvements, data quality testing, and large-scale data product delivery. Collaborate across teams, promote data stewardship, and support batch and streaming pipelines while deploying, serving, and monitoring machine learning models.

Own the architecture and technical direction of Northbeam’s data ingestion and integration platform. Design scalable API-based ETL, event-driven and batch processing systems; establish standards for observability, freshness, and failure handling; and guide build-versus-buy decisions. Collaborate across engineering, infrastructure, product, and business teams while mentoring engineers, leading design reviews, and driving complex platform initiatives to completion.

Builds data pipelines, models, analytics solutions, AI and machine learning models, and Power BI dashboards using structured and unstructured learning data. Partners with faculty to gather requirements, generate insights into student behavior, apply statistical and diagnostic analysis, and provide dashboard training and analytics knowledge transfer to staff.

Designs and builds scalable data platforms and pipelines for scientific, clinical, manufacturing, and process-engineering data. Responsibilities include developing batch and streaming ETL/ELT systems, integrating Databricks, Snowflake, cloud platforms, MES, SCADA, and laboratory systems, and enforcing governance and GxP compliance. The role provides hands-on technical leadership, establishes data engineering standards, and collaborates with scientists, process engineers, and platform teams. It requires three days onsite weekly in Indianapolis and unrestricted U.S. work authorization.

Own the architecture, reliability, governance, and evolution of the Databricks lakehouse. Design batch and streaming pipelines, develop SQL/Python/PySpark transformations, productionize analytical and AI prototypes, and implement testing, orchestration, monitoring, data-quality controls, governance, and access management. Collaborate with analysts, Engineering, and Product to deliver scalable, cost-effective data solutions for clinical trial insights.

As the founding engineer of the Data Intelligence team, you will design semantic layers, develop AI tooling, and automate data processes to drive business insights.

Design, build, and operate scalable batch and streaming data pipelines and analytical data models. Lead data architecture across ingestion, transformation, storage, and serving layers while implementing testing, CI/CD, observability, quality, lineage, governance, and access controls. Integrate restaurant and enterprise data sources, optimize platform performance and cost, deliver production-ready datasets for BI and AI, mentor engineers, and collaborate with cross-functional technical stakeholders.

Summer internship focused on building scalable data architectures and pipelines, automating data processes, improving data quality frameworks, and supporting cloud infrastructure. Work includes AWS automation, data warehousing, workflow optimization, platform integration, and operationalizing tools to process structured and unstructured data while leveraging analytics, AI/ML, and standardized pipelines.

Develop and maintain data engineering tools, services, ETL pipelines, ingestion processes, data transformations, extraction tools, and enterprise data models. Analyze structured and unstructured source data, optimize query performance, process large data volumes, collaborate with data scientists, and provide project documentation.

Owns clinical data operations by building and maintaining ELT pipelines, warehouse models, data contracts, quality controls, reconciliations, dashboards, and monitoring. Integrates clinical, laboratory, sequencing, and corporate data into validated, audit-ready systems. Supports production reliability, partner data transfers, regulatory compliance, root-cause analysis, documentation, stakeholder collaboration, and technical guidance for research and development teams.

Designs, develops, and optimizes enterprise Snowflake data platforms, pipelines, data models, and ELT/ETL workflows. Builds reliable data lake and warehouse solutions, manages Snowflake performance and costs, implements data quality and governance practices, and supports cloud data environments on Azure or AWS. Partners with analysts, scientists, engineers, and business stakeholders to deliver scalable solutions, troubleshoot platform issues, influence architecture, and mentor junior engineers.

Build and operationalize scalable cloud data pipelines and infrastructure, primarily on GCP. Responsibilities include ETL/ELT development, complex data integration, data architecture, process automation, analytics tooling, performance optimization, CI/CD support, and collaboration with engineers, data scientists, product managers, and business stakeholders. The role focuses on healthcare and clinical data, including data platforms, operational systems, and infrastructure supporting business insights.

Own and evolve the data infrastructure powering identity and fraud products. Build and operate scalable ETL/ELT pipelines, ensure data quality and observability, optimize storage and access, support product squads, respond to production incidents, mentor engineers, and drive data platform innovation.

Build and maintain scalable batch and real-time ETL processes, data warehouses, lakes, and marts. Develop Power BI and SSRS reports and dashboards, automate data workflows, optimize queries, support secure data exchanges, protect sensitive information, and improve data quality and system stability. The role requires SQL and relational database expertise, collaboration with business users and data teams, and a focus on reliable, maintainable solutions. This is an entry-level campus recruiting position for May 2027 offers.

Designs and enhances SQL Server databases, writes SQL and Transact-SQL procedures, manages ETL and ELT pipelines, troubleshoots data issues, optimizes database performance, monitors system health, and collaborates with engineers, data scientists, and business stakeholders to support scalable data flows and reporting.

Build and operate scalable Python-first ETL and ELT pipelines for genomic and operational data. Integrate LIMS, laboratory instruments, applications, and third-party sources while guiding architecture and engineering standards. Own data quality, lineage, observability, reproducibility, audit readiness, security, and regulatory compliance across hybrid and cloud environments. Work with SQL databases, workflow orchestration, containers, Kubernetes, AWS data services, distributed processing, streaming, bioinformatics workflows, and infrastructure as code.

Design, deploy, secure, and optimize enterprise database environments supporting mission-critical ArcGIS systems. Responsibilities include database architecture, configuration, backup and recovery, high availability, disaster recovery, performance tuning, access controls, automation, data loading, and integration. The role operates across cloud, on-premises, and hybrid infrastructures and provides technical guidance to project teams and customers.

Designs and maintains healthcare data pipelines, ETL workflows, databases, data models, and analytical frameworks. Analyzes claims and payer data to identify trends and insights, while ensuring data quality and integrity. Creates reports, dashboards, and visualizations for nontechnical stakeholders and collaborates with healthcare policy experts, analysts, and other stakeholders to translate requirements into data solutions. The role also improves data infrastructure and analytics capabilities.

Entry-level rotational Data Engineer role focused on SQL Server development, SSIS package creation, data validation, ETL, performance tuning, and cross-platform integrations (Salesforce, Marketing Cloud). Participate in SDLC, code reviews, data profiling, reporting, and stakeholder meetings with the goal of permanent placement after rotations.

Design, build, and maintain cloud-native AWS data platforms and scalable data lake/warehouse solutions. Develop and deploy data pipelines for structured and unstructured data to support AI/ML and BI, lead cross-functional teams, and manage cloud migration and modernization efforts. TS/SCI with polygraph and a CS bachelors degree required.

Architect and operate BILL’s enterprise data platform across ingestion, lake storage, batch and streaming processing, query, feature store, knowledge graph, and search layers. Drive platform-wide technical decisions, migrations, standards, reliability, cost efficiency, and self-service capabilities. Partner with ML, Risk, Payments, and Analytics teams to deliver scalable solutions, lead complex initiatives from inception through production, and mentor senior and staff engineers.

Designs, builds, and maintains scalable batch and streaming data pipelines and datasets in Databricks using Python, Spark, and SQL. Supports SQL Server, SSIS, SSRS, and data warehousing environments while improving performance, reliability, data quality, and scalability. Integrates enterprise data sources, enables reporting and AI/ML workloads, and partners with business and IT teams to deliver technical data solutions.

Designs, builds, and maintains scalable batch and streaming data pipelines and datasets in Databricks using Python, Spark, SQL, and Delta Lake. Supports SQL Server, SSIS, and SSRS environments while improving performance, reliability, data quality, and scalability. Integrates enterprise data sources, applies data warehousing practices, and partners with business users, analysts, and IT teams to deliver reporting, analytics, and AI/ML solutions.