Top Senior Data Engineer Jobs

Build and manage complex ETL and data pipelines, data warehouses, monitoring, and alerting systems for a legal-focused infrastructure team. Lead cross-functional data engineering initiatives from planning through delivery, optimize large-scale data processing, identify reliability issues, and communicate with technical and non-technical stakeholders. The role also involves supporting consulting practice activities, documentation, thought leadership, and client-facing deliverables.

Design, implement, and optimize large-scale data processing on Databricks and Spark. Build ETL pipelines, integrate data across AWS services (Redshift, RDS), orchestrate workflows with Airflow, and ensure data warehousing and engineering best practices. Lead and mentor development teams and collaborate with cross-functional stakeholders to drive data strategy and execution.

Senior Data Engineer responsible for Epic and EHR integrations supporting value-based care, risk and quality workflows, member attribution, roster management, and care-gap tools. The role designs and executes EHR development tasks, coordinates with clinical, data, operations, and development teams, documents business and data flows, evaluates AI and automation opportunities, and develops reporting to identify data-quality issues and prevent outages.

Designs, builds, and maintains enterprise data pipelines, ETL processes, integrations, data infrastructure, and complex data models. The role supports analytics partners, develops scalable data warehouse solutions, tunes SQL and databases, troubleshoots production issues, and collaborates with architects, analysts, developers, and stakeholders. Responsibilities include end-to-end project delivery, data modeling, big data engineering, production support, agile planning, and mentoring team members.

Build and implement SSAS environments, redesign reports, develop SQL queries and Power BI dashboards, and apply tabular, relational, and multidimensional data modeling. The role transforms and pre-aggregates data, documents logical and physical models, develops reporting solutions, and collaborates with federal client technical teams. Expertise in ETL, data pipelines, data warehousing, SQL, SSIS, SSAS, Power BI, and cloud environments is required.

Designs, develops, and maintains enterprise Oracle databases and data processes. Responsibilities include database performance and capacity monitoring, SQL and PL/SQL optimization, schema development, replication, disaster recovery, security controls, performance testing, and migrating on-premises databases to cloud data platforms.

Own and scale Collective’s data platform by building reliable batch and event-driven pipelines into BigQuery, modeling data with dbt, and enforcing quality, observability, security, and cost standards. Partner with engineers, analysts, and business stakeholders to deliver trustworthy datasets, metrics, reporting, and AI-enabled warehouse access. The role includes production ownership, incident response, governance, infrastructure practices, and support for analytics and LLM-based tools.

Designs, develops, and maintains scalable AWS data platforms, data lakes, and pipelines. Builds workflows with Glue, S3, Lambda, and Step Functions; implements governance and access controls through Lake Formation; automates infrastructure with Terraform; and develops Python and Boto3 integrations. Manages Snowflake platforms, CI/CD deployments, testing, monitoring, troubleshooting, and production optimization using CloudWatch and Splunk. Collaborates with engineering and architecture stakeholders while using AI-assisted development tools.

Designs and maintains SAP BW data models, pipelines, ETL processes, and analytics integrations. Leads migration of SAP BW 7.x assets to SAP Business Data Cloud using SAP Datasphere and SAP Analytics Cloud, converts legacy objects into CDS-based semantic models, optimizes data flows, supports reporting migration, and ensures data quality, governance, lineage, and operational reliability across SAP and non-SAP environments.

Own and maintain Nebraska’s enterprise Medicaid encounter-processing and data-ingestion platform. Responsibilities include Scala and Spark development, Drools rule maintenance, Hive batch workflows, production support, defect resolution, releases, performance optimization, documentation, and Cloudera Data Platform administration. The role requires troubleshooting distributed data applications, supporting Linux production environments, managing deployments, and coordinating infrastructure operations.

Build and operate data ingestion, transformation, orchestration, and quality pipelines; design dimensional and semantic data models; and develop and administer Power BI reports, dashboards, and workspaces. Partner with stakeholders to define requirements and metrics, document lineage, support self-service analytics, and contribute to solution design, presales, standards, and reusable components.

Leads data architecture, data warehouse design, ETL development, and scalable analytics solutions using cloud and modern data technologies. Develops Python, SQL, and JavaScript code; optimizes distributed systems and CI/CD pipelines; troubleshoots production systems; evaluates emerging technologies and AI tooling; collaborates with technical and business stakeholders; and mentors junior engineers.

Build and operate event-driven data pipelines processing healthcare claims data for fraud, waste, and abuse detection. Operationalize validated fraud signals, maintain alert delivery and partner dashboards, monitor pipeline reliability, and collaborate with data architects, scientists, and investigative teams. The role requires production-scale pipeline engineering using AWS, Snowflake, Python, and SQL in a fully remote environment.

Own and evolve UJET’s data platform, including MySQL CDC replication to BigQuery, dbt transformations, Terraform infrastructure, pipelines, data quality, observability, and Looker models. Build reliable, analytics-ready datasets and trusted metrics for internal and customer-facing reporting. Partner with Analytics, Finance, Product, and Engineering to deliver scalable data solutions, improve platform reliability, optimize performance and costs, and establish development standards and guardrails.

Build and operate reliable data systems and products supporting clinical operations, drug evaluation, business development, analytics, machine learning, and AI agents. Responsibilities include designing pipelines, canonical data models, data contracts, warehouse transformations, quality and observability practices, governance for regulated data, and incident response. The role partners closely with Product Engineering and Data Science, uses AI-assisted development, contributes to architecture, and mentors other engineers.

Builds scalable ELT pipelines, data models, business metrics, and semantic layers supporting analytics, reporting, and AI/ML applications. The role ingests data into Snowflake, develops dbt and SQL transformations, validates data quality and governance, and partners with product managers, analysts, data scientists, visualization teams, and business stakeholders. Responsibilities include harmonizing media data, delivering ready-to-use datasets, and supporting strategic decision-making across television production, distribution, and marketing.

Designs, builds, and scales data pipelines across raw, staging, curated, and serving layers. Develops Azure Data Factory ingestion and transformation workflows for APIs, files, JSON, XML, and SFTP sources; handles pagination, authentication, schema drift, validation, and backfills. Models semi-structured data in Snowflake, contributes to CI/CD deployments, establishes pipeline standards, and mentors data engineers while partnering with analytics, product, and engineering teams.

Build and maintain data pipelines using ETL/ELT, Databricks, Python, Spark, and Airflow. Manage data transformations, databases, schemas, and data quality controls. Support reporting, analytics, and machine learning integrations while deploying solutions in AWS. Automate workflows, troubleshoot data issues, document processes, and collaborate with data scientists, analysts, database administrators, and business stakeholders.

Leads mission data integration and engineering initiatives supporting SOUTHCOM’s Common Intelligence and Operational Pictures. Responsibilities include building ETL pipelines, ingesting and transforming data, managing Databricks, Maven Smart System, and War Data Platform solutions, developing machine learning and statistical models, validating data quality, supporting secure interoperability, collaborating with intelligence and operational teams, delivering training, and documenting analytical processes.

Build and optimize scalable data architectures, pipelines, warehouse tables, and data feeds from diverse sources. Ensure data quality, monitoring, CI/CD reliability, security, and efficient compute and storage usage. Collaborate with engineering, product, and cross-functional teams on data analysis, AI, machine learning, experimentation, and ad technology products. Develop automated data integration and cleansing processes, troubleshoot pipeline failures, and contribute to complex projects involving cloud platforms, MLOps, and large datasets.

Owns customer-facing data engineering engagements from discovery through delivery. Leads workshops, presents to technical and executive stakeholders, translates business needs into data, analytics, and AI solutions, and builds applied AI prototypes. The role also manages scoping, estimates, risks, and cross-functional delivery with Sales, Product, and Engineering. Required expertise includes SQL, cloud data warehouses, Python, dbt, data governance, Git, CI/CD, and strong customer communication.

Design, build, and maintain scalable cloud data pipelines and platform components to power analytics, ML, and applications. Implement transformations, data quality checks, lineage and governance, optimize performance and cost, monitor platform health, and build reusable components across business units while collaborating with product and architecture teams.

Build, deploy, and operationalize complex data pipelines and solutions using AWS, dbt, and ETL tools. Design data architectures, models, transformations, and quality controls; analyze data sources; support AI, machine learning, and business intelligence initiatives; resolve defects and incidents; test data movement and transformation code; and collaborate with teams and business users on data products.

Develop, operate, and maintain distributed parallel streaming and batch data-processing pipelines in classified environments. Responsibilities include configuring data connections, parsing, normalizing, mapping, modeling, enriching, correlating, and deduplicating data; integrating data and analytics SDKs/APIs; applying Agile SDLC practices; maintaining CI/CD pipelines; and updating technical documentation, procedures, and training materials.

Designs, builds, tests, deploys, and maintains secure ETL/ELT pipelines for public health data. Integrates Power Platform, Azure services, APIs, SQL Server, CDC/NIOSH systems, and approved file-transfer platforms. Develops batch, event-driven, and near-real-time data workflows supporting intake, validation, reporting, analytics, notifications, and results delivery while meeting FISMA Moderate, federal privacy, Agile, and DevSecOps requirements.