Top Data & Analytics Jobs

Lead design and delivery of scalable, secure cloud data pipelines and products. Translate requirements into architecture, apply advanced data modeling (Data Vault 2.0/dimensional), embed data quality, observability, security, and DataOps/CI-CD practices, optimize performance and cost, mentor engineers, and enable AI/ML-ready datasets within an agile DevSecOps model.

Build and deliver custom data solutions for major clients using LiveRamp’s platform. Responsibilities include client discovery, requirements analysis, pipeline development and troubleshooting, technical consultation, project management, presentations, and coordination across business, engineering, product, and client teams. The role requires learning complex data technologies quickly, communicating technical concepts clearly, and managing multiple implementations simultaneously.

Designs and builds ETL/ELT pipelines and data architectures across hybrid big-data and traditional database environments. Develops dimensional and normalized data models, ingests structured and unstructured sources, maintains data quality and security, troubleshoots pipeline issues, and delivers analytical reports and dashboards using Cognos and Tableau. Collaborates in agile teams, manages change requests, audits data integrity, and applies testing, documentation, and development best practices.

Supports ETL pipeline development, legacy data migration, data transformation, validation, profiling, cleaning, and integration for a secure DEA modernization program. Collaborates with technical and business teams, monitors pipeline performance, troubleshoots issues, documents data flows and migration results, and participates in Agile development activities under senior engineer guidance.

Lead architecture and implementation of end-to-end data and analytics platforms for CoreAI. Design and optimize large-scale ETL/ELT pipelines, ensure data governance, quality, lineage, security, and enable self-service analytics. Collaborate with product, engineering, AI research, and business stakeholders to translate needs into roadmaps and data solutions. Mentor senior engineers and drive engineering standards, operational rigor, and scalable analytics for AI workloads.

Designs and maintains scalable ETL pipelines for structured and unstructured data, supporting DEA legacy-data migration, validation, integration, and modernization. The role improves data quality, interoperability, reliability, and cloud-platform performance while preparing data for analytics and AI/ML use cases. Responsibilities include source-to-target mapping, troubleshooting, documentation, stakeholder collaboration, and participation in Agile development.

Lead enterprise Microsoft Fabric data engineering initiatives, including architecture, ETL/ELT pipelines, data modeling, Power BI semantic models, performance optimization, security, governance, DevOps automation, AI enablement, production support, and technical team mentoring.

Leads data engineering for a functional area by defining future-state architecture, establishing technical standards, mentoring team members, developing code and procedures, fixing bugs, maintaining quality, gathering requirements, and supporting application functionality. The role serves as a subject matter expert, partners with architecture and application engineering leaders, and drives technology adoption and professional development.

Builds and maintains ETL/ELT data pipelines and architecture for analytics. Responsibilities include monitoring jobs, troubleshooting loads, managing tickets, discovering and ingesting data from databases, APIs, files, and third-party applications, and transforming structured and unstructured data. The role also performs data modeling, quality audits, software testing, documentation, and agile project delivery while collaborating with technical teams and stakeholders.

Lead enterprise-scale data engineering initiatives, including the design, development, deployment, and maintenance of production data pipelines and scalable data platforms. Guide multidisciplinary teams, modernize analytics workflows using Python and SQL, develop shared libraries, and support Palantir Foundry adoption through training, integration, and architecture. Collaborate with stakeholders, provide technical presentations, and apply expertise in data architecture, Databricks, cloud platforms, and distributed data processing.

Develop manufacturing data processes, dashboards, reports, and analytics that improve safety, efficiency, throughput, reliability, and operational stability. Clean and validate data, maintain data standards, translate findings into actions, and support digital solution adoption. Partner with Operations, Industrial Engineering, Manufacturing Systems Engineering, and Continuous Improvement teams on data gaps, labor models, capacity planning, staffing analysis, bottleneck reduction, material flow, and standard work improvements.

Designs, develops, and deploys scalable AI-enabled data solutions for Department of Defense missions. Builds data pipelines, preprocessing workflows, feature engineering processes, machine learning services, and visualization tools in secure containerized environments. Collaborates with product managers, developers, platform engineers, and mission stakeholders while applying data validation, testing, reproducibility, and continuous delivery practices. Requires work in a SCIF and strong technical communication skills.

Develop and optimize scalable Python-based ETL pipelines and data services for a Microsoft Azure enterprise data environment. Ingest, transform, validate, and govern structured and unstructured data while implementing schema enforcement, quality checks, monitoring, and performance tuning. Support Azure data infrastructure, workflow orchestration, APIs, and microservices for analytics and machine learning readiness. Collaborate with data scientists, analysts, architects, and governance teams to ensure reliable, compliant, reproducible, and auditable data operations.

Designs, implements, and supports enterprise data warehouses, data marts, operational data stores, ETL frameworks, and data integration solutions. Responsibilities include source analysis, data profiling, pipeline development, data mapping, transformation design, architecture documentation, testing, quality validation, deployment coordination, performance optimization, and regulatory compliance. The role collaborates with architects, business users, operations teams, and cross-functional partners while using Talend and cloud, big data, database, and scheduling technologies.

Lead graph data engineering to design, implement, and scale Neo4j-based graph platforms. Build graph models, ingestion pipelines (batch and streaming), integrate graphs with analytics/ML, establish governance, and enable graph-driven business use cases like fraud detection and Customer 360.

Build, deploy, and maintain reliable data infrastructure, including ingestion and ETL/ELT pipelines, data quality processes, warehouses, automation, orchestration, and lightweight data applications. Optimize data stack performance, document pipelines, and collaborate with analysts, data scientists, and stakeholders. Curate datasets supporting operational models, feature stores, and AI/ML workflows while continuously learning data engineering best practices.

Design, build, test, and maintain scalable ETL/ELT pipelines and cloud data solutions (Azure, Databricks, Synapse, Snowflake). Optimize SQL/C# data processing, implement data validation and CI/CD, leverage AI tools to automate workflows, collaborate with stakeholders, and support SharePoint environments.

Architect, build, and operate low-latency market data systems, including feed handlers, normalization, distribution, and exchange connectivity. Design high-throughput infrastructure for real-time trading data, improve reliability and performance, participate in observability, on-call, and incident response, and deliver well-tested features end to end. Partner with engineering, product, and cross-functional stakeholders while leading a lean, high-impact team.

Designs, builds, and operates scalable data systems and products on Cargill’s AWS-native Minerva platform. Develops batch and streaming pipelines, data models, lakehouse architectures, automated deployment pipelines, and governed analytics solutions. Migrates workloads from legacy CDP and Impala platforms, validating schemas, row counts, aggregates, and historical data accuracy. Collaborates with data and analytics teams across supply chain, procurement, manufacturing, commercial, and LATAM domains.

Administer, optimize, and modernize enterprise SQL Server, Azure SQL, MongoDB, and MongoDB Atlas environments. Responsibilities include performance tuning, high availability, disaster recovery, security, monitoring, migrations, automation, architecture guidance, and on-call support. The role also leads database projects and adopts AI-assisted engineering solutions to improve reliability and operational efficiency.

Build and maintain governed Silver/Gold data models and semantic assets on Databricks. Translate stakeholder KPIs into testable, reusable transformations, apply enterprise modeling standards, enable domain self-service, ensure data sensitivity and governance, and review domain-contributed models for production readiness.

Build Sailor Health’s foundational data platform from scratch. Responsibilities include developing Snowflake and dbt pipelines integrating EHR, CRM, and communications data; creating data-powered web applications; automating clinical and billing workflows with Python; analyzing therapy transcripts and patient data using AI and machine learning; tracking clinical outcomes; and designing scalable data architectures in collaboration with clinicians, engineers, founders, and other teams.

Design, build, and own the full data platform (ingestion, modeling, CI/CD, orchestration, observability, and BI). Implement streaming and batch pipelines for IoT data, enable governed self-service, enforce data governance/privacy/security, evolve AI access patterns, and provide technical leadership and mentorship to data engineers and data scientists to deliver production-ready analytics and ML capabilities.

Design, build, and own scalable ETL pipelines, dashboards, and data quality processes. Troubleshoot discrepancies, expand the cloud data platform, implement CI/CD and IaC practices, and collaborate with stakeholders, developers, and data scientists to deliver BI solutions.

The Data Engineer will design and build data pipelines and platforms, mentor teams, and implement AI/ML solutions for various high-impact projects, primarily in secure environments.