Top Data Engineer Jobs

Design, build, and operate large-scale, fault-tolerant real-time and batch data pipelines powering an ad-tech DSP. Collaborate with engineers and data scientists to define data models and SLAs, optimize performance and capacity, enforce data quality and monitoring, mentor engineers, and evaluate new data technologies to scale analytics and ML workloads.

Designs secure Azure data pipelines and analytics foundations using Databricks and ADLS Gen2. Responsibilities include modernizing Oracle workloads, developing medallion data models, implementing data quality and monitoring practices, integrating curated datasets with Power BI, and establishing secure Azure Government identity, network, storage, and access patterns. The role also requires documentation, operational support, stakeholder collaboration, and performance optimization.

Leads data engineering strategy and mentors an analytics engineering team. Designs scalable ELT pipelines, warehouse integrations, data models, API ingestion workflows, and data quality frameworks using cloud and database technologies. Conducts exploratory analysis, supports dashboards and analytical solutions, monitors warehouse performance and costs, and ensures data governance, security, and compliance. Collaborates with data scientists, analysts, engineers, and business stakeholders to deliver healthcare analytics solutions.

Designs and optimizes batch and streaming data pipelines, cloud data warehouses, dimensional data models, and ETL/ELT processes at terabyte scale. Troubleshoots pipeline issues, improves infrastructure reliability and performance, implements data quality and governance practices, and supports privacy compliance. Collaborates with analysts, data scientists, and stakeholders to translate business needs into technical solutions. Leads and mentors data engineers, documents systems, and evaluates emerging technologies.

Own and evolve Stream’s end-to-end revenue operations data platform. Build Python and dltHub ingestion pipelines, SQLMesh transformation models, dimensional data models, quality and observability systems, and GCP infrastructure centered on BigQuery. Integrate GTM systems, enable trusted analytics and reverse ETL, improve reliability and cost efficiency, and establish engineering standards. The role requires hands-on platform ownership in a fast-moving hybrid Boulder startup environment.

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.

Build and own the unified data layer: ingestion pipelines into ClickHouse, data modeling and entity resolution, canonical metric definitions, quality checks, and internal data tools. Support ad-hoc analyses and work closely with engineering to ensure stable, trustworthy data powering customer dashboards and product BI.

Own SQL and dbt-based data modeling and data quality for clinical and claims datasets. Author and refactor dbt models/tests, triage DQ alerts, design monitors, optimize SQL performance, and collaborate with Data Engineering and stakeholders to ensure trusted production datasets.

Design and operate a scalable data platform for high-volume robotics telemetry and control-plane data. Responsibilities include data capture, storage tiers, ingestion, transformation engines, durable processing, schema and ID-key management, retention and egress policies, replication, CI/CD, monitoring, and alerting. The role supports cloud, on-premises, edge, and outage-resilient deployments while enabling downstream analytics and machine learning.

Build scalable ETL/ELT pipelines in Databricks using PySpark, Spark SQL, Delta Lake, Delta Live Tables, Workflows, and Unity Catalog. Ingest batch and streaming data across medallion architecture layers, develop transformations and data quality rules, and implement orchestration, monitoring, alerting, and automation. The role requires cloud data engineering, data modeling, performance tuning, CI/CD, and strong knowledge of lakehouse architecture, governance, and distributed systems.

Build scalable production-grade ETL/ELT pipelines on Databricks using PySpark, Spark SQL, Delta Lake, Delta Live Tables, and Workflows. Develop data ingestion, transformations, quality rules, orchestration, monitoring, and automation across Bronze, Silver, and Gold layers. Apply dimensional data modeling, streaming, cloud platforms, CI/CD, governance, security, performance tuning, and lakehouse architecture. Explain technical concepts and architecture decisions using real project examples.

Lead design, build, and maintain scalable data pipelines, ETL processes, data models, and storage solutions. Support production data ecosystem, optimize performance, ensure data quality and security, integrate data sources/APIs, and provide technical guidance to teams while collaborating with stakeholders.

The Lead Data Engineer will design, implement, and maintain data pipelines for mission-relevant data, ensuring compliance with security frameworks and optimizing data accessibility and interoperability across mission systems.

Design, build, and deploy data pipelines, databases, and AI-enabled automation to improve manufacturing test throughput, yield, and reliability. Integrate test systems and IoT sensors, support root-cause analysis, develop dashboards and alerts, and work hands-on with production teams for on-floor troubleshooting and sensor/validation tasks.

Design and maintain production data pipelines and architecture across Snowflake and Databricks, scale Terraform deployments, implement data governance, build demo data processes, and collaborate cross-functionally to support analytics and product use cases.

Design, build, and operate production data pipelines and architectures across Snowflake and Databricks. Scale demo data across cloud warehouses, partner with infrastructure for high availability, implement data governance, and collaborate cross-functionally to support analytics and GTM use cases.

As a Data Engineer, you will optimize data pipelines, develop code in Python and Spark, manage cloud infrastructure, and drive data solutions.

As a Data Engineer, you will manage data pipelines, automate processes, and collaborate on software solutions to optimize supply chain performance.

Embed with the Asia Commodities desk to own vendor data feeds, build scalable data pipelines and dashboards, transform market and supply/demand data into analytical constructs, support research-to-production workflows, and collaborate with investors and platform teams to enable systematic commodities strategies and trading decisions.

Supports and enhances enterprise data systems, databases, integrations, and analytics platforms. Responsibilities include SQL development, database administration, performance tuning, production troubleshooting, backup and recovery, enterprise integrations, Power BI and SSRS reporting, and Microsoft Fabric pipeline and workspace support. The role also contributes to data modeling, migration, governance, security, documentation, compliance, and collaboration with application developers, infrastructure teams, vendors, analysts, and business stakeholders.

Design, develop, and maintain scalable data platforms, ETL/ELT pipelines, and tabular/dimensional data models across on-premises and cloud environments. Build and schedule data processing jobs, ensure data quality, performance tuning, and troubleshooting. Lead code reviews, participate in on-call production support, mentor engineers, and drive best practices and technical debt remediation.

Lead the data engineering strategy and architecture for distributed pharmaceutical data systems. Design and scale pipelines, APIs, and data platforms while ensuring reliability, governance, quality, and PII/PHI protection. Drive technical decisions, improve developer efficiency, deliver business impact, influence cross-functional roadmaps, mentor engineers, communicate with executives, and promote organizational adoption of new technologies and processes.

Supports the Army’s cPROBE data repositories and database layer by designing, implementing, and maintaining reliable, secure, high-performance data infrastructure. Responsibilities include managing interfaces and APIs, minimizing data access latency, defining data and metadata requirements, designing data warehouses, implementing business rules, facilitating change control, and conducting data verification and testing. The role also coordinates with architects, programmers, analysts, and engineers while providing product support and maintenance.

Leads enterprise data engineering and AI-ready data knowledge initiatives across Network Services. Designs scalable pipelines, data models, metadata, governance, lineage, quality controls, and validation frameworks for structured and unstructured data. Partners with subject matter experts to prepare trusted context for AI and agentic workflows, guides hybrid data architectures, supports testing and releases, establishes control standards, and mentors data engineers.

Lead design, build, and maintain enterprise-scale data platforms and pipelines. Mentor cross-functional teams, develop software to process structured/unstructured data, and operationalize ETL/ELT workflows for analytics in agile environments supporting mission-driven projects.