Top Data & Analytics Jobs

Leads the design and implementation of enterprise data platforms, pipelines, ingestion systems, and analytics products. Develops production-grade Python applications, manages AWS, Snowflake, and dbt environments, establishes engineering and governance standards, and improves observability, security, quality, and cost efficiency. Integrates disparate data sources and supports generative AI, RAG, and LLM applications while partnering with business, software, DevOps, and data science teams.

Develop interactive dashboards, visual analytics platforms, and reporting solutions for SATCOM, network operations, and engineering data. Integrate AI/ML model outputs into user-facing analytics, establish visualization standards, guide architecture decisions, and collaborate with multidisciplinary engineering and data science teams. Mentor junior personnel and present technical insights to customers, senior leaders, and stakeholders.

Build and operate reliable data pipelines transforming flight test, simulation, and operational data into versioned, traceable ML training datasets. Responsibilities include ingestion, normalization, time alignment, validation, dataset lineage, anomaly mining, labeling workflows, deployment feedback loops, monitoring, and data coverage analysis. Collaborate with flight test and operations teams on instrumentation and data access. The role is on-site in Boston and requires production data engineering experience supporting machine learning pipelines.

Own enterprise-scale data architecture and engineering for Xometry’s data platform, including real-time partner integration pipelines connecting manufacturing systems. Design scalable batch and streaming systems, data models, governance, observability, testing, and CI/CD practices. Lead multi-quarter technical roadmaps, collaborate across engineering, product, analytics, machine learning, and external partners, and mentor engineers. Support the DFM AI and IQE integration across Teamcenter, Designcenter, and related systems.

Lead enterprise-scale data architecture and engineering for Xometry’s Data Platform, including scalable batch and streaming pipelines, partner integrations, data models, governance, observability, and low-latency event-driven flows. Own the data plane connecting Xometry with Teamcenter and Designcenter, establish engineering standards, develop technical roadmaps, collaborate across teams, and mentor engineers. The role requires strong SQL, Python, cloud data warehouse, AWS, modern data tooling, CI/CD, testing, and enterprise integration expertise.

Leads the design, governance, and integration of mission-focused data ontologies, knowledge models, taxonomies, and standards. Collaborates with analysts, engineers, architects, and data owners to establish interoperable data practices, support standards adoption, and integrate models into enterprise tools and workflows. Produces monthly posture reports and guides data architecture and approval processes for intelligence data enterprises.

Build and maintain Oracle EBS data replication and integration pipelines using GoldenGate and Qlik Replicate for Advana. Monitor performance, troubleshoot failures, optimize throughput and latency, document data flows, support BI dashboards, and ensure compliance with federal data governance and DoD cybersecurity requirements. Coordinate upgrades, patching, migrations, and collaboration with reporting, security, and IA teams.

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.

Leads development of Navanta’s secure data backbone, including lakehouse architecture, bank-system ingestion, CDC pipelines, semantic modeling, data quality, reconciliation, observability, governance, and lineage. Partners with AI/ML, security, and platform teams to deliver reliable, queryable data products for financial institutions. Owns data contracts, access controls, regulatory data handling, pipeline SLAs, bank onboarding, and on-call reliability operations.

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.

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.

Own and evolve Worldly’s production data lake, Postgres warehouse, CDC and streaming pipelines, dbt transformations, orchestration, BI access controls, and graph integrations. Maintain reliable, governed analytics infrastructure using Iceberg, Trino, Dagster, Terraform, and AWS. Support production generative AI workflows involving embeddings, similarity search, extraction, and classification. Responsibilities include data quality, monitoring, security, incident response, disaster recovery, schema governance, and collaboration with data science and analytics stakeholders.

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.

Designs, builds, and maintains an ontology-grounded enterprise semantic map and metadata graph. Develops automated metadata ingestion, ETL pipelines, API integrations, graph database schemas, lineage and provenance models, and graph queries. Aligns data assets with enterprise ontologies, governance requirements, and mission metadata. Enables semantic search and AI agents, optimizes graph performance, documents architecture, and mentors engineers. Requires production data engineering, graph database, systems integration, and graph query experience, plus an active TS/SCI clearance.

Supports development and maintenance of an ontology-grounded enterprise metadata graph. Responsibilities include automating data-source discovery, ingesting technical metadata, integrating APIs and catalogs, mapping assets to ontologies and provenance layers, maintaining graph databases, supporting lineage and governance metadata, writing basic graph queries, and enabling semantic search and AI-agent workflows. Collaborates with senior engineers and AI teams to connect data assets to enterprise mission workflows.

Design, implement, and maintain secure, scalable enterprise database and geospatial data platforms for mission-critical Defense and Intelligence systems. Responsibilities include database architecture, configuration, backup and recovery, high availability, disaster recovery, performance tuning, security controls, automation, data loading, migrations, integrations, and technical guidance. The consultant collaborates with customers and cross-functional teams throughout implementation and communicates solutions to technical and non-technical stakeholders.

Design and implement secure, scalable geospatial database platforms for defense and intelligence operations. Responsibilities include database administration, performance tuning, backup and recovery, high availability, disaster recovery, access controls, compliance, automation, data integration, and technical guidance. The role also involves collaborating with customers and cross-functional teams throughout requirements, design, implementation, and deployment.

Designs, implements, secures, and optimizes scalable enterprise database and geospatial data platforms for mission-critical Defense and Intelligence systems. Responsibilities include database architecture, configuration, backup and recovery, high availability, disaster recovery, performance tuning, access controls, compliance, automation, data loading, migrations, integrations, and customer-facing technical guidance.

Manage, design, and optimize data activities for federal IAM systems. Develop data models, file layouts, source-to-target mappings, data dictionaries, integrations, ETL processes, and analytics support for identity analysis and fraud detection. Administer and tune databases, including configuration, backups, monitoring, capacity planning, and performance optimization. Support secure handling of identity and PII data, network-related data flows, documentation, token management processes, and collaboration across development, security, and operations teams.

As a Data Engineer, you will create efficient data models, pipelines, and analytics frameworks, collaborating with various departments to transform raw data into actionable insights.

Build and maintain reliable ETL and real-time data pipelines, enable data-driven product decisions, ensure relevant event collection, align data roadmap to business goals, and collaborate with platform, security, and analytics teams to deliver actionable insights.

Develops and maintains healthcare data integration and EDI solutions, including ETL processes, data pipelines, data validation, cleansing, transformation, reporting, and compliance testing. Troubleshoots applications, manages code reviews, documents systems, and collaborates with analysts, healthcare providers, business partners, and IT teams. The role also implements security measures and recommends improvements to integration processes.

Designs, builds, and optimizes cloud data solutions using Microsoft Fabric and Azure. Develops data pipelines, Lakehouse architectures, ELT/ETL processes, data models, semantic models, and Power BI reporting. Partners with stakeholders to define requirements and technical designs, supports platform migrations, and implements data quality, governance, security, and monitoring practices. The role also contributes to AI and machine learning data initiatives and requires strong SQL, Python, PySpark, and modern cloud data engineering expertise.

Design, maintain, and troubleshoot Databricks notebooks, CI integrations, and GitLab repositories. Build APIs and integrations (Databricks, GitLab, Confluence), resolve access and Python library issues, support auditing and engineering standards, and assist leadership with evaluating and implementing tools to improve engineering visibility and workflows.