Top Data & Analytics Jobs

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.

Job posting contains only company and EOE information. No Data Engineer responsibilities, qualifications, or requirements are provided in the description.

Build and operate production data pipelines across Oracle, Talend, and Apigee using SQL, PL/SQL, Java, and Python. Integrate acquisition and financial data, develop reusable data products and analytics datasets, maintain REST APIs, migrate legacy data, optimize workflows, and implement validation, governance, testing, documentation, and database design practices. Support Navy acquisition transformation in an IL-4/5 government environment.

Lead data engineering teams in designing, developing, testing, implementing, and supporting cloud-based data solutions. Build distributed, full-stack, machine-learning-enabled systems using Java, Scala, Python, relational and NoSQL databases, streaming technologies, and cloud data warehouses. Collaborate with product managers and Agile teams, conduct code reviews and unit testing, optimize performance, mentor engineers, and adopt emerging technologies and interactive AI tools.

Builds and maintains reliable data solutions, datasets, transformations, and enterprise data warehouse infrastructure. Responsibilities include data quality profiling, cleansing, normalization, modeling, testing, CI/CD, performance optimization, anomaly analysis, and troubleshooting. The role partners with data architecture teams, supports reporting and KPI accessibility, and follows PII and organizational risk standards. It requires onsite work in West Bend, Wisconsin, with some hybrid flexibility.

Designs, develops, and maintains scalable data pipelines, warehouses, lakes, and integrated analytics platforms using Kafka, Elastic, AWS, OpenShift, and related technologies. Optimizes ingestion, queries, indexing, storage, and cluster performance while automating ETL/ELT, validation, monitoring, and CI/CD processes. Supports Kibana dashboards and visualizations, troubleshoots platform issues, documents architectures and procedures, and collaborates with analysts and engineering stakeholders. The role supports DISA at Scott Air Force Base and requires DoD security compliance.

Design, build, and maintain ETL/ELT pipelines and SSAS tabular models. Develop complex T-SQL queries and Power BI reports. Ensure data quality, optimize performance, and support migration of legacy systems into Microsoft BI environments while collaborating with stakeholders.

Designs and maintains Microsoft Fabric data pipelines, medallion Lakehouse architecture, data models, governance, orchestration, and quality processes. Integrates ERP, HRIS, MRP, finance, project, employee, service, and manufacturing data into reliable data products. Supports reporting, business intelligence, AI and machine learning use cases, including RAG and vector embeddings. Troubleshoots pipeline issues, manages Fabric capacity and documentation, and collaborates with analysts, data scientists, developers, DevOps teams, and business system owners.

Data Engineer Interns will build and support data pipelines, workflows, transformations, and quality processes for analytics, reporting, automation, and AI initiatives. They will work with structured and unstructured data, cloud platforms, databases, and engineering tools while collaborating with cross-functional teams. The internship provides mentorship, Agile team exposure, and hands-on experience applying Python, SQL, ETL, data modeling, and emerging AI technologies to insurance business needs.

Data Engineer Intern supporting data pipelines, workflows, transformations, quality monitoring, and cloud-based data platforms. The intern will help prepare structured and unstructured data for analytics, reporting, automation, machine learning, and AI solutions. Responsibilities include collaborating with cross-functional teams, applying Python and SQL-based engineering techniques, validating data, and using AI tools to improve productivity. The role provides mentorship and hands-on experience within The Hartford’s technology and data organization.

Supports government space and counterspace intelligence operations by collecting, integrating, validating, maintaining, and analyzing large datasets. Develops automated data workflows, scripts, databases, dashboards, reports, and visualizations; establishes data quality and governance practices; analyzes RF and signals data; and supports collection management, mission planning, operational reporting, and readiness assessments. Collaborates with government stakeholders, mission partners, and technical teams.

Designs, implements, secures, monitors, and optimizes AWS cloud-native data engineering solutions supporting analytics, AI/ML, LLMs, and agentic services. Builds ingestion, ETL, transformation, data-store, API, search, monitoring, FinOps, and security capabilities. Prepares model-ready datasets, applies DoD Zero Trust and NIST controls, supports authorization activities, documents architectures and costs, and collaborates with government, data science, cybersecurity, and cloud engineering stakeholders onsite in a classified environment.

Build and operate data-intensive web products and APIs, extend high-throughput ETL pipelines and scrapers, ensure reliability/observability, design self-scaling services, participate in code review, and collaborate across product, data, and infrastructure teams to meet production SLAs.

Design, build, and maintain scalable production data pipelines and cloud-based solutions in Microsoft Azure. Develop Python, SQL, Spark, Spark SQL, and PySpark workflows for data ingestion, transformation, integration, analytics, reporting, and machine learning. Monitor, troubleshoot, validate, and optimize data pipelines and large-scale processing workloads while collaborating with technical teams and stakeholders. Document architectures and follow software engineering best practices for testing, version control, and deployment.

Designs, administers, and optimizes data systems supporting FSA IAM applications. Develops migration plans from legacy AIMS and PAS systems, creates data models, mappings, layouts, dictionaries, and integration processes, and supports analytics and fraud detection. Manages database configuration, performance tuning, monitoring, backups, capacity, secure data handling, encryption, network data flows, token management, and documentation while collaborating with development, security, and operations teams.

Design, build, and operate large-scale data platforms and Spark/PySpark pipelines. Enable data integration, modeling, quality, and observability. Build MCP servers and AI-augmented tooling, mentor engineers, and lead cross-functional projects to deliver reliable data products.

Build scalable cloud data infrastructure and pipelines to transform transactional and financial data into reporting-ready datasets. Ensure accuracy, traceability, governance, and observability; partner with cross-functional teams, mentor engineers, and leverage AI-assisted tools to improve engineering productivity and enable AI-powered applications.

As a Lead Data Engineer, you will design and implement data solutions using big data technologies, collaborate with Agile teams, and mentor other engineers.