Top Senior Data Engineer Jobs

Lead the design and development of scalable data pipelines, microservices, cloud-native ingestion systems, and streaming solutions. Collaborate with architects, engineers, product teams, and business stakeholders to build reliable data platforms using Python, Java, AWS services, SQL, and relational and NoSQL databases. Establish data quality, testing, monitoring, automation, and validation frameworks while optimizing performance and resolving production issues across distributed systems.

Develop and maintain scalable data pipelines and secure infrastructure for multimodal synthetic data, AI evaluation, real-time model monitoring, and clinical research data. Responsibilities include data transformation, preprocessing, quality assurance, database management, privacy compliance, interactive dashboards, pipeline documentation, and scientific dissemination.

Leads data engineering initiatives embedded with infrastructure teams. Designs governed data products, integrations, data contracts, quality controls, metadata, lineage, and standardized data models. Reconciles infrastructure asset inventories, resolves data discrepancies, and partners with engineering, product, and governance stakeholders to improve data completeness, accuracy, interoperability, security controls, and enterprise analytics.

Build scalable, governed analytical data products, semantic layers, reusable data models, certified datasets, and AI-ready analytics foundations. Perform validation, quality checks, optimization, documentation, and lineage management. Collaborate with Analytics, Product, Engineering, Data Engineering, and business stakeholders on project planning and metric governance. Improve self-service reporting, dashboard performance, automation, and data consistency while mentoring junior team members.

Senior Data Engineer responsible for Snowflake infrastructure and architecture, performance engineering, production support, automation, DevOps, lifecycle management, security, compliance, and AI-powered solutions. The role includes monitoring, backup and recovery, provisioning, patching, upgrades, capacity administration, incident and change management, SQL development, Python coding, documentation, and operational team training. This is a U.S.-based telecommuting position with on-call responsibilities.

Designs, develops, and optimizes scalable data platforms, pipelines, ETL/ELT workflows, and Lakehouse architectures using Azure Databricks, Spark, and cloud services. Builds data models, quality and governance frameworks, integrations, and secure access controls. Optimizes workloads, supports streaming and analytics initiatives, troubleshoots pipelines, and contributes to CI/CD, DevOps, infrastructure automation, documentation, and technical leadership.

Build and maintain ETL/ELT pipelines to move product data into analytics-ready stores (Postgres, data lake, warehouse). Design and optimize data models, ensure data quality and documentation, support ad-hoc research requests, and collaborate with research and engineering teams to enable reproducible analytics.

Lead design and build scalable data processing and control-plane solutions on Databricks/AWS. Develop Python/PySpark/SQL pipelines, APIs/SDKs, governance, observability, CI/CD, and operational runbooks. Mentor engineers, drive adoption of AI-assisted development, and ensure data quality, security, lineage, and platform reliability.

Design, develop, and support scalable ETL frameworks and data integration pipelines for complex healthcare datasets in Oracle Exadata environments. Responsibilities include data mapping, transformation, performance tuning, testing, troubleshooting, automation, documentation, data profiling, validation, reconciliation, and quality assurance. The role also supports secure coding standards, code reviews, operational reliability, and integration with modern cloud data platforms.

Build and maintain cloud data pipelines, analytics platforms, data models, and AI-powered solutions using Azure, Airflow, Databricks, Snowflake, and Power BI. Secure cloud environments through Azure networking, PIM, RBAC, and vulnerability remediation. Collaborate with infrastructure and delivery teams on deployment, monitoring, automation, responsible AI adoption, and continuous platform improvement.

Build and scale data products supporting analytics, engineering services, and AI/ML models. Responsibilities include optimizing queries and data pipelines, developing data models and governance processes, administering ETL/ELT and reverse ETL orchestration, and creating documentation and training. The role also involves mentoring, interviewing, and hiring data engineers while helping define the organization’s data engineering strategy.

Designs, develops, and maintains data platforms, pipelines, and integration solutions across on-premises and cloud environments. Responsibilities include resolving data quality and security issues, building ELT processes, developing data scripts and relational or non-relational models, and monitoring data lake performance and scalability. The role also supports analytical and operational decision-making through reliable data solutions and collaborates with cross-functional teams.

Leads large-scale data engineering initiatives, overseeing end-to-end delivery and mentoring junior engineers. Designs scalable, secure data architectures; automates data ingestion, transformation, and deployment; monitors pipelines; and resolves technical issues. The role requires advanced distributed data processing, data modeling, data warehousing, cloud platforms, analytics tools, CI/CD, DevOps, SQL, Python, and data pipeline technologies.

Build and own Snout’s data foundation, including PostgreSQL and dbt warehouses, ingestion pipelines, dimensional models, data quality, documentation, and reliable rebuilds. Reconcile data from veterinary practice-management systems, support LLM/AI tooling, translate stakeholder needs into technical plans, and establish the foundation for a future data team and analyst.

Architects, deploys, and operates large-scale Kafka and Confluent streaming platforms. Responsibilities include cluster sizing, configuration, security, automation, observability, HA/DR, troubleshooting, developer enablement, and production reliability. Partners with application and cross-functional teams, translates requirements into scalable solutions, contributes to code and design reviews, and mentors junior engineers. Requires deep Kafka expertise, DevOps/SRE practices, infrastructure-as-code experience, and strong communication skills.

Designs, implements, maintains, and secures Azure-based data platforms. Develops ETL/ELT pipelines, data warehouses, data lakes, data models, and cloud integrations. Administers Azure data services and databases, including performance tuning, backup, recovery, high availability, and disaster recovery. Automates operational tasks, supports CI/CD, monitors platform performance, resolves production incidents, and provides technical support to business and development teams.

Leads enterprise data engineering strategy and architecture, designing scalable cloud data platforms, pipelines, models, governance, and AI-assisted engineering solutions. Establishes technical standards, security, observability, and reusable frameworks; influences architecture decisions across teams without direct management. Mentors senior engineers, partners with business and technical stakeholders, and remains hands-on with cloud technologies, software architecture, data modeling, and platform design.

Build and operate secure batch and streaming data pipelines across structured, semi-structured, and legacy sources. Responsibilities include data ingestion, integration, quality validation, governance, monitoring, alerting, workflow orchestration, deployment automation, troubleshooting, and provenance management. Collaborate with engineering, cloud, data science, security, architecture, and customer teams to deliver reliable data products and AI-enabled workflows in regulated environments.

Designs, develops, and maintains scalable data pipelines, ETL/ELT workflows, and data platforms for Intelligence Community missions. Transforms structured and unstructured data using Python, SQL, Scala, or Java; supports ingestion, processing, monitoring, reliability, and data quality. Collaborates with analysts, developers, and data consumers in Agile teams, troubleshoots complex data environments, and translates mission requirements into technical solutions. The role is on-site in Chantilly, Virginia and requires an active TS/SCI clearance with polygraph.

Designs, builds, deploys, and maintains scalable data platforms and ETL/ELT pipelines. Processes structured, unstructured, batch, streaming, and event-based data while optimizing data lake architecture and cloud performance. Collaborates with analysts, engineers, developers, and data consumers on mission-driven projects. Responsibilities include data modeling, governance, cataloging, lifecycle management, automation, platform tuning, and developing data solutions for applications such as fraud detection, cancer research, and national intelligence.

Build and maintain workflow applications, relational data structures, data pipelines, SQL logic, REST integrations, dashboards, and reporting outputs for a federal health client. Translate user stories into functionality, perform testing and validation, support Agile delivery, documentation, code reviews, deployments, and compliance requirements. Collaborate with technical and business stakeholders in a remote environment, with occasional client travel.

Design, develop, and operate large-scale data ingestion, transformation, enrichment, and processing pipelines across cloud, hybrid, and on-premises environments. Build distributed services using Java, Python, and Kafka; optimize data models and platform performance; deploy applications on Kubernetes; support infrastructure automation and CI/CD; troubleshoot production systems; and collaborate with customers and solution architects on implementations.

Designs, develops, and maintains scalable data warehouses, data lakes, platforms, and ETL/ELT pipelines. Builds batch and streaming workflows, processes structured and unstructured data, optimizes cloud storage and pipeline performance, and implements data governance and lifecycle management. Guides multidisciplinary teams in agile environments while delivering mission-focused data engineering solutions.

Design, build, optimize, and operate scalable ETL/ELT pipelines supporting petabyte-scale legacy-to-cloud data migration. Develop data models, mappings, schemas, and data quality processes for structured and unstructured datasets. Improve automation, performance, reliability, and fault tolerance while supporting AWS security and compliance requirements. Collaborate with QA, Cloud, DevSecOps, and migration teams; document workflows; track migration progress; and mentor engineers. This is a full-time, on-site role in Huntsville, Alabama requiring active Top Secret clearance.

Develops geospatial and enterprise data solutions supporting national security analytics. Responsibilities include integrating and governing data, managing metadata and metrics, querying databases with SQL, accelerating large-scale transactions on GPU architectures, and applying Python data science and machine learning libraries. The role also produces assessments, briefings, reports, and intelligence products while coordinating projects, personnel, working groups, and cross-community stakeholders. An active TS/SCI clearance with SSBI is required.