Top Senior Data Engineer Jobs

Design, build, and scale TaxRise’s data infrastructure on AWS using Databricks. Own end-to-end pipelines, ETL/ELT processes, data modeling, orchestration, governance, cluster optimization, and delivery for reporting and analytics. Collaborate with the Chief Data Officer, Salesforce developers, data engineers, and full-stack developers to establish platform standards and support business growth.

Designs and maintains scalable data architectures, ETL workflows, batch and real-time pipelines, and SQL/NoSQL storage for intelligence operations. Ensures data quality, governance, security, compliance, documentation, and lineage across multi-INT datasets. Collaborates with analysts and technical teams to translate intelligence requirements into secure solutions and participates in mission planning and coordination forums.

Designs, develops, and optimizes scalable Azure-based data engineering solutions, including lakehouses, warehouses, ETL/ELT pipelines, Databricks notebooks, and analytics workflows. The role supports enterprise reporting, AI and machine learning initiatives, feature engineering, Generative AI, and MLOps. Responsibilities include performance tuning, CI/CD automation, production support, technical design, lifecycle management, stakeholder collaboration, and translating business requirements into secure, reliable data platforms.

Build, deploy, and operationalize data pipelines and solutions supporting AI, machine learning, and business intelligence. Responsibilities include data transformation, cleansing, governance, security, quality management, source analysis, testing, incident resolution, and defect remediation. The role collaborates across teams, supports data products, educates users, and applies software engineering and DevOps practices to deliver reliable analytical data solutions.

Leads a data engineering team and sets the strategic direction for scalable, secure data initiatives. Oversees project planning, resource allocation, engineering standards, documentation, technical debt, and complex issue resolution. Partners with business leaders, data scientists, and IT teams to translate organizational needs into technical solutions. Requires expertise in AWS, cloud data platforms, data modeling, governance, orchestration, real-time processing, programming, and enterprise data management.

Build and configure a government Databricks workspace, develop ingestion pipelines from enterprise systems, and prepare data for reporting. Implement Unity Catalog governance, metadata, auditing, and lineage; configure MLflow workflows; assess security controls for sensitive data; and create architecture documentation, data dictionaries, and operating procedures. The role also requires integrating legacy systems, optimizing Delta Lake pipelines, and supporting long-term platform sustainment.

The Data Engineer will develop and document research data products, contribute to scientific papers and findings, lead research efforts, coordinate with academic partners, and propose independent research goals. The role also includes project management, technical documentation, meetings, and collaboration with statistical developers, data engineers, and researchers. Experience with Apache Airflow, SQL, data visualization, web development, statistics, analytics, and agile software development is desired.

Build and operate production-grade AML data products, ETL pipelines, data models, integrations, and ML-ready datasets. Ensure data quality, lineage, security, governance, observability, reliability, and regulatory compliance. Support transaction monitoring, customer segmentation, risk rating, sanctions screening, and financial crime prevention initiatives. Lead architecture decisions, production support, incident response, CI/CD improvements, automated testing, code reviews, mentoring, and engineering standards.

Designs and maintains ELT pipelines, warehouse integrations, data models, and data quality processes. Collaborates with data scientists, analysts, engineers, and stakeholders to deliver scalable analytical solutions. Responsibilities include data integration, governance, security, performance optimization, exploratory analysis, visualization, and cloud-based API ingestion. Requires strong SQL, DBT, Snowflake, Azure Data Factory, cloud, and scripting experience, plus leadership and communication skills.

Designs and maintains scalable data pipelines, data quality frameworks, and cloud-native data solutions using Spark, Databricks, Hadoop, AWS, and advanced SQL. Implements validation, monitoring, observability, and automated remediation capabilities. Applies AI/ML, anomaly detection, and statistical modeling to improve data quality. Provides production support, resolves complex incidents, contributes to architecture and engineering standards, and collaborates with cross-functional data and engineering teams.

Design, build, and maintain batch and streaming data pipelines; ingest, transform, and optimize data for analytics and BI; ensure data quality, reliability, and performance; implement monitoring/alerting; support MLOps and analytics workloads; collaborate with data scientists and BI teams; document AI systems and RAG/Cortex-based solutions.

Build and maintain data pipelines and integrations across engineering, manufacturing, procurement, ERP, PLM, and business systems. Develop monitoring, dashboards, automation, and data governance practices that improve operational decision-making. Collaborate with cross-functional teams to translate requirements into durable tools, document system ownership and lineage, and support evolving organizational needs. This is a full-time, onsite role in Denver, Colorado.

Build, optimize, and maintain scalable data pipelines and reusable datasets for business intelligence, marketing, and data science. Develop federated data validation frameworks, optimize queries and data models, and protect user privacy and security. The role uses Python, SQL, AWS, Airflow, PostgreSQL, Iceberg, Snowflake, Spark, and other modern data-stack technologies. It is based in New York City with in-office work three days per week.

Leads enterprise data engineering for NASIC, designing data structures, pipelines, standards, automation, and architecture. Advises senior leadership on data engineering and analytics, guides cross-functional personnel, defines business requirements, and manages projects from inception through implementation. Requires expertise in Tableau, SharePoint, business analytics, Python, SQL, ETL, Power BI, and data preparation, along with a current Top Secret clearance with SCI adjudication and U.S. citizenship.

Designs and operates scalable data pipelines and platforms for analytics, machine learning, and Agentic AI. Develops production-grade Python and SQL solutions, implements data quality and monitoring, supports end-to-end ETL/ELT workflows, and partners with data scientists, analysts, and business stakeholders. The role also includes code reviews, testing, source control, production support, mentoring, and establishing engineering best practices. Familiarity with Celonis, SAP, and Salesforce is preferred.

Build and operate reliable ETL and ELT pipelines across lakehouse and transactional systems. Develop data models, enforce governance, integrate diverse data sources, and support analytics, APIs, and predictive tools. Monitor performance, data quality, lineage, cost, and uptime while collaborating with data science, AI, product, and innovation teams.

Build and govern Anthropic’s quote-to-cash data foundation. Design canonical models from Salesforce, CPQ, and billing data; establish data integrity standards and SLAs; collaborate with systems engineers on schema changes and ingestion; and create data products, dashboards, and self-service analytics tools for GTM and Finance teams. The role also owns data architecture, translates stakeholder needs into technical requirements, and influences roadmaps.

Build and operate scalable healthcare data pipelines, dbt models, event-driven services, APIs, and Google Cloud infrastructure. Model and govern multimodal EHR, genomic, and imaging data for AI agents across federated hospital networks. Ensure data quality, lineage, observability, security, HIPAA compliance, and production reliability while contributing to TypeScript application services and agentic workflows.

Leads customer-facing enterprise data engineering engagements for federal clients, designing scalable data architectures, pipelines, integrations, APIs, object storage solutions, and Java-based applications. Responsibilities include architecture workshops, technical implementation, troubleshooting, documentation, customer communication, mentoring junior engineers, and service innovation. The role requires active Top Secret clearance, at least eight years of relevant experience, strong consulting skills, and expertise across enterprise software, data engineering, and integration technologies.

Designs, builds, and maintains data infrastructure, ETL pipelines, data models, and analytics data warehouses. Ensures accurate data flow from creation through presentation layers and supports reporting, analytics, and machine learning initiatives. Collaborates with stakeholders to deliver data products, improves engineering tooling and pipelines, participates in on-call support, and uses cloud, containerization, orchestration, streaming, and CI/CD technologies.

Develop and maintain ETL pipelines supporting data ingestion, transformation, preparation, labeling, querying, and analytics. Troubleshoot data and system issues at scale, work with structured and unstructured data, and support government and cybersecurity data operations. Collaborate within an agile team using Linux, cloud, Kubernetes, and data-flow technologies while ensuring quality, testing, and timely delivery.

Designs and maintains scalable data pipelines, models, warehouses, lakehouses, and cloud-based data platforms. Builds ETL/ELT workflows, supports AI/ML data needs, implements data quality and governance controls, and manages batch and streaming integrations. Collaborates with technical and business teams to deliver reliable data solutions, troubleshoot infrastructure, and improve scalability, performance, and business value.

Leads technical strategy for enterprise data pipelines and data-sharing platforms. Builds scalable, resilient, high-performance systems using AWS, lakehouse architecture, Kafka, Flink, Spark, Snowflake, and Databricks. Develops code, drives engineering excellence and technology adoption, influences enterprise stakeholders, advises on platform capabilities, mentors engineers, and recruits technical talent. The role requires deep data engineering and architecture expertise, hands-on leadership, and innovation across internal and external data environments.

Designs and develops scalable software products, data pipelines, ETL workflows, APIs, and modular services for ingesting, transforming, processing, and managing data. The role applies Node.js or Python, microservices, distributed systems, NoSQL data stores, large language models, and MCP servers within an Agile team supporting mission-critical operational and analytical systems. Requires a TS/SCI clearance with polygraph, U.S. citizenship, and onsite work in Chantilly, Virginia.

The Principal Data Engineer will lead data engineering efforts on public cloud, design scalable solutions, build data pipelines, and drive deliverables with a high level of technical expertise in relevant technologies.