Top Data & Analytics Jobs

Builds and maintains data pipelines, dashboards, databases, Linux infrastructure, and automated reporting tools supporting battery R&D and product testing. Responsibilities include managing laboratory data transfers and metadata, developing research and experiment tracking dashboards, integrating data solutions for battery system testing, supporting custom reporting, and collaborating with R&D teams on continual tool improvements.

Designs, develops, and maintains scalable AWS data platforms, data lakes, and pipelines using Glue, S3, Lambda, Step Functions, Python, Snowflake, and Terraform. Implements governance and security through Lake Formation, automates infrastructure and deployments with CI/CD, and monitors production workloads using CloudWatch and Splunk. Collaborates with engineering and architecture teams to deliver reliable data solutions, leveraging modern AI-assisted development tools.

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.

Lead the design, development, and maintenance of scalable data systems and pipelines for visual analytics. Build high-performance relational databases, transform structured and semi-structured data, integrate enterprise data warehouses, and improve data quality and system performance. Establish database, programming, and security standards while supporting secure data practices and zero trust principles. The role requires collaboration across teams and experience with government or multi-enclave environments is preferred.

Build and lead scalable data and machine learning systems for an AI platform automating product regulatory compliance. Responsibilities include designing system architecture, selecting technologies, developing backend and internal services, collaborating with data and ML engineers, and balancing speed with scalability. The role requires strong Python, database, microservices, AWS, and Docker expertise, along with startup product development experience and technical leadership.

Develop and maintain Master Data Management, data governance, stewardship, quality, and reference data processes. Partner with enterprise stakeholders and governance groups to define standards, validate master data, establish quality rules, support data owners, and prioritize initiatives. Lead requirements for system configuration changes while using data profiling, modeling, and analytics to improve data validity and consistency across systems.

Develop and maintain master data management and integration capabilities, including data quality rules, governance processes, stewardship workflows, data validation, profiling, modeling, and integration design. Collaborate with enterprise stakeholders, data owners, stewards, and governance groups to define requirements, configure systems, and support strategic MDM initiatives. Use Python, SQL, AWS services, APIs, Dataiku, and ETL/MDM tools to improve data accuracy, consistency, transformation, and retention.

Develop and advance the enterprise data platform by gathering business requirements, designing data models and warehouses, and building data visualization solutions. Administer reporting, analytics, and visualization platforms while promoting best practices. Collaborate cross-functionally, apply business and process knowledge, manage multiple projects, communicate strategically, and provide technical leadership in data engineering and business intelligence.

Build and operate Sinclair’s enterprise Snowflake data platform, developing production pipelines, integrations, ELT models, data quality controls, observability, governance, and performance optimizations. The role requires hands-on Snowflake, Openflow, SQL, and Python expertise, production troubleshooting, enterprise integrations, deployment practices, and collaboration across technical and business teams. This is an on-site position in Hunt Valley, Maryland.

Design, build, and operate Databricks Lakehouse pipelines (Bronze/Silver/Gold) using Delta Lake and DLT. Optimize Spark/SQL workloads and Delta tables for performance and cost, implement incremental processing with exactly-once semantics, ensure data quality, and deliver documented datasets and dashboards (Power BI/other) with SLAs. Collaborate with cross-functional teams, integrate real-time sensor data, and support stakeholders. Occasional travel; primarily on-site.

Develops automation, data pipelines, databases, analytics, machine learning models, reporting tools, and web applications for package technology quality assurance. Analyzes manufacturing, product, and reliability data to support risk assessment and root-cause investigations. Collaborates with engineering teams, communicates findings, trains users, maintains software and reports, and shares analytics best practices across global teams.

Designs and maintains data architecture, canonical schemas, ETL/ELT pipelines, cloud data warehouse integrations, governance standards, data lineage, and documentation. Partners with engineering, product, business teams, and vendors to ensure reliable, trustworthy data. Designs data access patterns for AI and LLM applications, including retrieval-augmented generation and minimum-necessary-data principles.

Design, build, test, deploy, and support governed data products and pipelines for supply chain operations. Develop Snowflake and dbt models, optimize workloads, implement data quality and observability controls, and use Databricks and Spark for scalable analytics and AI-ready data foundations. Partner with product, engineering, analytics, and business teams to define requirements and data contracts, lead initiatives through production, document solutions, and improve reliability, security, and engineering standards.

Build and scale foundational data infrastructure for AI-powered hardware and consumer devices. Responsibilities include developing reliable pipelines, integrating manufacturing, telemetry, quality, and customer operations data, creating durable data models, enabling manufacturing and product intelligence, and establishing data engineering standards. The role requires strong architectural judgment, distributed systems knowledge, production infrastructure experience, and cross-functional collaboration in an early-stage organization.

Build and operate governed data pipelines using Microsoft Fabric, Azure, Python, PySpark, Spark, and SQL. Implement Medallion Architecture, ingest data from APIs and enterprise systems, and develop lakehouse and Azure SQL data products supporting analytics and AI. Use Git and CI/CD for deployments, explore AI agentic workloads, and partner with architects and stakeholders to translate requirements into scalable solutions. The role requires local travel, occasional overtime and weekends, and in-person work flexibility.

Designs and maintains scalable data pipelines and architectures for operational, intelligence, and mission data. Develops dashboards, visualizations, data models, and AI-enabled analytics using Python, Maven Smart System, Palantir, and Power BI. Performs data integration, cleansing, scraping, exploitation, and predictive analysis while supporting C-UAS operations, assessments, acquisition decisions, and strategic planning. Produces technical documentation and executive briefings, ensures data governance and security, and collaborates with mission stakeholders.

Designs, develops, and maintains Databricks-based ETL pipelines, data lake architectures, datasets, and data warehouses supporting reporting, analytics, and AI/ML workloads. Troubleshoots and optimizes Databricks and SSIS processes, maintains SSRS reports, implements data quality controls, migrates legacy systems, and collaborates with business and technical teams. The role also promotes AI-assisted development practices and documents technical procedures.

Leads end-to-end data enablement for Supply Chain and Manufacturing domains. Partners with business and technical stakeholders to define use cases, roadmaps, data models, semantic layers, integration requirements, quality controls, and governed data products. Builds and optimizes Databricks lakehouse pipelines using SQL, Python, PySpark, and Delta Lake across complex ERP, manufacturing, procurement, inventory, and finance systems. Establishes standards for lineage, security, documentation, reliability, and production readiness.

Designs and productionizes hybrid knowledge graph solutions for healthcare and clinical data using LPG and RDF technologies. Builds FHIR transformation and ingestion pipelines, implements ontology mappings, entity linking, identifiers, provenance, validation, and graph queries with Cypher or SPARQL. Collaborates with ontology, terminology, FHIR, clinical, and data engineering teams, while contributing to architecture reviews, code reviews, testing, and engineering best practices.

Build and operate production data pipelines integrating ERP/ATS/project management/telemetry into a queryable layer. Own the data model for a live knowledge graph. Ship SLA-backed datasets/services for tools and ML. Convert unstructured vendor/field data (PDFs, spreadsheets) into trusted structured inputs. Ensure data quality via tests, monitoring, and lineage, and use modern data stacks and LLM tools responsibly.

Develops and maintains SQL data extracts, validation rules, financial reports, and Power BI or Tableau dashboards for a federal financial management program. Produces recurring status and trend visuals, supports change and release management using JIRA, maintains SharePoint resources, ensures Section 508 accessibility and sensitive-data protection, and creates user guidance. The role also supports data quality checks, process improvement, and ad hoc reporting tasks.

Own data engineering delivery for multiple clients, including designing and maintaining SQL and Python ETL/ELT pipelines, integrations, data warehouses, and Airflow orchestration. Translate business requirements into scalable data solutions, manage scope and priorities, troubleshoot data integrity and performance issues, and establish quality controls and documentation. Serve as a client-facing technical lead, guide architecture through influence, and use AI-assisted coding tools while supporting team best practices.

Design, develop, and maintain Customer Data Platform solutions and data pipelines to create unified customer profiles, manage identity resolution, enable audience segmentation and activation, ensure data quality and governance, and support integrations with marketing, CRM, and cloud data systems.

Build and support dependable, high-volume ingestion pipelines for APIs, files, database extracts, and scraping sources. Develop safe incremental loads, backfills, retries, data quarantine, lineage, privacy controls, reusable connectors, onboarding automation, monitoring, and recovery runbooks. Own integrations in production and create modules that enable other teams to onboard sources without managing platform infrastructure.

Lead data engineering for client reporting, measurement, internal operations, and AI-driven initiatives. Build and maintain Snowflake pipelines and dbt models, integrate marketing and business data, troubleshoot issues, and maintain governance, privacy, and consent controls. Develop semantic layers and governed access for AI tools. Shape architecture and technical roadmaps, translate business needs into data solutions, and build and lead a data support team.