Top Data & Analytics Jobs

Design and maintain ELT/ETL pipelines and cloud data warehouse models, build Power BI reports, apply AI tools to accelerate data workflows, and partner with business stakeholders to deliver reliable analytics and data governance.

Design, develop, and monitor large-scale data pipelines and cloud-native data applications; collaborate with consulting teams and clients; apply best practices (TDD, CI/CD); build and operationalize ML-ready data platforms; produce documentation and runbooks; mentor junior engineers and contribute to proposals and analytics assets.

Design, build, and support data pipelines and workflows to clean, integrate, extract, analyze and deliver structured and unstructured data. Implement and customize TRSS client solutions and POCs, provide solution architecture input, automate internal processes (including RPA), and support customer-facing deployments. Collaborate with product, software engineering, data science, and business teams to meet unique client requirements and integrate TRSS content into client applications.

Build and maintain Snowflake-based data warehouse and lakehouse components. Develop and optimize dbt models and SQL transformations, implement and monitor data pipelines, support CI/CD and orchestration workflows, contribute to cloud and Snowflake configuration, improve data quality and reliability, troubleshoot issues, and document data models and runbooks while collaborating with platform and analytics teams.

Build and maintain centralized pricing data infrastructure, scalable workflows, and data pipelines; integrate AI capabilities; deliver pricing tools, reporting, and governance to support data-driven pricing and budgeting across the firm.

Intern will explore and clean unfamiliar datasets, write code to ingest, transform, and monitor data quality using Python, SQL, and OCaml, work with structured and unstructured data, complete two owned projects, and be mentored by full-time Data Engineers.

As a Data Engineer, you will design, build, and maintain scalable data platforms, working on mission-driven projects that analyze big data to derive insights. You will collaborate with multi-disciplinary teams in a fast-paced environment.

Build and scale core data infrastructure and foundational datasets. Design, develop, and maintain scalable data pipelines for analytics, ML, experimentation, and product use cases. Implement data quality, audits, alerting, and anomaly detection. Partner with product, engineering, and business stakeholders and help establish long-term, scalable data architecture and tooling.

Build and operate production data pipelines that ingest, validate, transform, and govern clinical and operational data. Deliver governed metrics, self-service analytics, and observability across teams. Partner with engineering, product, clinical, and GTM to ensure accurate, reproducible, and defensible metrics; automate ingestion, validation, and transformation using modern tooling and cloud data warehousing.

Integrate operational data into enterprise data warehouses; design, optimize, and support SSIS ETL pipelines; develop SSRS and Power BI reporting solutions and reusable data models; deliver data migrations and self-service extracts. Design production AI/ML solutions using Python, OCR, AWS Bedrock, and Claude, including multi-agent LLM implementations. Partner with stakeholders, lead code reviews, mentor developers, and support production BI deployments.

The Senior Data Engineer will architect and enhance data infrastructure, create dependable data pipelines, oversee cloud-based data warehouses, and mentor engineers to enable scalable solutions for analytics and machine learning.

Lead the design, development, optimization, and maintenance of scalable batch and streaming data pipelines. Build ETL/ELT workflows using Python, SQL, and AWS services; optimize data models and cloud data platforms; ensure data quality, integrity, reliability, and performance; establish CI/CD and infrastructure-as-code practices; resolve pipeline issues; document systems; and mentor junior engineers.

Develops data pipelines, analysis tools, dashboards, reports, spreadsheets, and customer briefings for military test events. Operates live display and analysis systems, collects and interprets live and post-mission test data, and provides real-time operational insights during field deployments of up to 25%.

Lead the technical strategy and architecture for a scalable clinical data platform. Drive cross-team engineering initiatives, redesign legacy data pipelines, build batch and streaming systems, develop deployment and self-service tooling, improve monitoring, manage data models and governance, ensure privacy and security, and mentor engineers. Partner with product, AI, infrastructure, health systems integration, and business intelligence teams to deliver reliable, extensible data capabilities.

Designs, builds, and optimizes scalable AWS and Snowflake data pipelines, infrastructure, data models, semantic layers, and data quality frameworks. Analyzes Medicare, Medicaid, and CMS repository data to support analytics and reporting. Responsibilities include workflow monitoring, automation, governance, security, performance optimization, stakeholder collaboration, and Agile delivery using modern DevOps and software engineering practices.

Designs and delivers production-grade data and AI solutions, including scalable pipelines, APIs, backend services, LLM and RAG integrations, and multi-agent systems. Owns architecture, data modeling, transformation, quality, evaluation, monitoring, and enterprise documentation. Collaborates with product, program, engineering, and business stakeholders to rapidly prototype solutions, influence technical direction, communicate trade-offs, and mentor engineers. Requires strong data engineering, Python, cloud, AI agent, MCP, and agentic framework experience.

Own end-to-end data engineering for a business domain, including API and other-source ingestion, dbt models, orchestration, data-quality testing, monitoring, documentation, and performance. Build reliable, AI-ready single sources of truth and ensure data freshness, accuracy, traceability, and scalability. Collaborate with stakeholders and the Director of Data, establish engineering patterns, review work, and mentor other engineers.

The Developer, Data and Integration maintains and develops scalable integration solutions focusing on B2B/EDI transactions, working with business units to translate data needs into technical solutions, and ensures the reliability and performance of data flows.

Owner of marketing-focused data engineering: build and maintain integrations, tracking, and pipelines across BigQuery, ClickHouse, GCS, and external systems; troubleshoot tracking, payment, and data quality; collaborate with cross-functional teams and document data flows.

The Data Engineer will build and optimize data pipelines and infrastructure, ensuring data quality for analytics and machine learning initiatives while collaborating with teams to develop data solutions.

Own and evolve the data lake and analytics stack powering fleet observability. Build and maintain scalable ETL/ELT pipelines, operate data lake infrastructure (Iceberg, Trino, Airflow, Spark, Superset), produce analytics and executive reporting, create dashboards and runbooks, and implement data governance and observability.

Own and scale end-to-end data infrastructure: build production ETL pipelines, design scalable schemas, enforce data quality/governance/security, create self-serve data models for analytics and ML, instrument observability, and partner cross-functionally while participating in on-call incident response.

As a Data Engineer at Booz Allen, you'll manage data engineering activities, develop scalable platforms, and work with multidisciplinary teams to extract meaningful insights from big data.

Support the Data team by building and maintaining scalable ETL pipelines and data infrastructure for multi-modal medical datasets, automate data QA, optimize data delivery on cloud platforms, and provide analytics tools and secure cross-border data storage.

Design and implement end-to-end data integration and warehousing solutions for large volumes. Implement Hadoop/Hive data movement, script in Linux shell and Python, develop SQL for integration and analysis, and perform high-volume DW performance tuning. Vertica experience is a plus. Requires technical independence and a BS in CS/EE.