Top Data & Analytics Jobs

Develop and maintain data cleansing reports, ETL pipelines, Alteryx workflows, PL/SQL processes, Power BI dashboards, and automated solutions supporting the VA FMBT program. Provide technical support to data analysts and developers, validate data quality and KPI integrity, coordinate requirements and testing with stakeholders, and maintain reporting, scorecards, and remediation metrics. The role requires federal financial management data experience, stakeholder collaboration, and the ability to obtain Public Trust clearance.

Build a canonical entity and knowledge graph for identity resolution, permissions, compliance, and agent authorization. Develop production data pipelines across internal systems, graph-based correlation, ML matching, schemas, APIs, provenance, bitemporal models, drift detection, and delegation credentials. Partner with Finance and Compliance on classification pilots while enforcing privacy, regulatory compliance, jurisdictional partitioning, and auditability.

Build and maintain Snowflake data pipelines, integrations, data models, reconciliation checks, backend services, and internal automation tools. Develop production AI workflows using Claude, MCP, and managed model platforms for document retrieval, extraction, and understanding. Own solutions through design, deployment, monitoring, documentation, and support while protecting sensitive financial data and collaborating with operations, compliance, vendors, and technical stakeholders.

Leads enterprise data engineering architecture and integration strategy for global data and AI solutions. Owns technical standards, roadmaps, API-first integrations, data pipelines, governance, observability, and cloud modernization using Azure, Databricks, and Microsoft Fabric. Partners across product, engineering, architecture, and business teams to deliver secure, scalable data products and AI platforms while mentoring technical talent and coordinating dependencies, compliance, and release readiness.

Designs, develops, maintains, and optimizes complex data systems, SQL/ETL processes, reporting solutions, and .NET applications supporting pharmacy benefit management. Responsibilities include database performance tuning, data warehousing, troubleshooting pharmacy claims, technical specification development, software testing, cloud integration, stakeholder collaboration, Agile participation, and mentoring. The role requires strong SQL Server, dimensional modeling, cloud database, Python, PowerShell, and healthcare data standards expertise.

Develops and supports ETL workflows, REST APIs, backend services, AWS file-transfer automation, legacy applications, and data-processing systems. Troubleshoots production issues, manages deployments across environments, improves monitoring and recovery, and documents technical systems. Requires SQL, relational database, API, data integration, batch-processing, and production support experience, along with U.S. citizenship and the ability to pass a Public Trust investigation.

Build and maintain Microsoft Fabric data pipelines, Lakehouse and Warehouse solutions, ingestion frameworks, Bronze/Silver/Gold data layers, transformations, dimensional models, and data-quality controls. Integrate ERP, CRM, manufacturing, construction, and other enterprise systems. Implement security, governance, DevOps, monitoring, documentation, and production support while partnering with architects, analysts, Power BI developers, and business stakeholders.

Design and implement data integration solutions, build data pipelines, manage cloud resources, and develop real-time data flows using AWS technologies and Snowflake.

Designs and maintains data architecture, ETL/ELT pipelines, data warehouses, lakes, and workflow automation. Ensures data quality, governance, security, availability, and performance while supporting reporting, analytics, dashboards, and machine learning. Translates business needs into technical data models, monitors data systems, and collaborates with Technology, Analytics, and business stakeholders. The role is based in the administrative office with work-from-home eligibility; travel is not required.

Designs and optimizes scalable data platforms, architectures, and production pipelines supporting AI and machine learning. Responsibilities include Databricks Lakehouse development, batch and real-time processing, data governance, security, monitoring, performance tuning, infrastructure planning, and collaboration with DevOps, MLOps, and cross-functional teams. The role serves as the AI team’s data engineering subject matter expert and ensures reliable, secure, cost-effective data solutions.

Designs and maintains scalable data pipelines, integration frameworks, databases, APIs, and enterprise data platforms. Develops data models and processing capabilities while ensuring quality, lineage, provenance, governance, security, and lifecycle management. Monitors performance, supports recovery and reconciliation, improves automation and reliability, and documents data standards and implementation guidance. Collaborates with engineering, analytics, and operational teams to deliver trusted data assets.

Leads a team of data engineers building and maintaining scalable data pipelines for Department of Defense applications. Sets technical strategy, enforces reusable templates and infrastructure-as-code standards, oversees Databricks pipeline development, improves monitoring and schema drift detection, conducts code reviews, and promotes agile practices. Partners with architects on modernization initiatives, including GPU-enabled transformations.

Build and maintain ETL pipelines, integrate data from multiple sources into warehouses, automate data collection, and perform cleansing, validation, and quality monitoring. The role supports on-premises and cloud analytics platforms, documents technical processes, and collaborates with Agile teams under the guidance of senior data engineers and data scientists.

Develop and maintain scalable data stores, ETL/ELT workflows, data pipelines, and platforms for structured and unstructured data. Build scripts and software for data retrieval, parsing, processing, and conversion. Collaborate with analysts, developers, and data consumers in an agile environment while supporting the design, deployment, monitoring, and operation of data platforms for mission-driven projects.

Supports data pipeline development, data modeling, wrangling, semantic layers, dashboards, and reporting using SQL, Python, AWS, and Snowflake. Collaborates with cross-functional teams to translate business requirements into data solutions and applies data governance, security, and quality practices. This co-op is designed for a currently enrolled university student available January 11 through June 25, 2027, and willing to relocate to Framingham, Massachusetts.

Supports the development and maintenance of SQL and Python data pipelines, data models, semantic layers, dashboards, and reports. Performs data wrangling and preprocessing, collaborates with cross-functional teams to translate business needs into data solutions, and applies data governance, security, and quality practices. The role provides hands-on data analytics engineering experience supporting business analytics and AI initiatives.

Supports the development and maintenance of SQL and Python data pipelines, data models, semantic layers, dashboards, and reports. Performs data wrangling and preprocessing, translates business requirements into data solutions, and contributes to data governance, security, and quality assurance. Collaborates with cross-functional teams supporting marketing, engineering, product development, supply chain, finance, business analytics, and AI initiatives.

Lead design and build of ETL pipelines and Gold Layer tables in Databricks; develop semantic layers, data dictionaries, and AI-ready documentation; create dashboards and ad-hoc analyses to surface marketing and sales KPIs; partner with stakeholders to translate requirements into analytics-ready datasets and enable self-serve BI and agentic AI workflows.

Lead data engineering activities by designing, building, deploying, and maintaining scalable data platforms and pipelines. Mentor multidisciplinary teams, oversee analytical data examination, guide enterprise software development, establish execution priorities, and support complex distributed applications in agile environments. The role requires Java, Spring, Linux, containers, CI/CD, and a TS/SCI clearance with polygraph.

Summer Data Engineering internship supporting the data engineering team. Candidates should be rising juniors or seniors studying Computer Science, BIS, MIS, or a related field, with basic Microsoft Word and Excel skills, 40 WPM typing speed, strong communication and organizational abilities, and the capacity to multitask.

Develop and maintain data pipelines, infrastructure, and integrations supporting analytics, data science, and business intelligence. Ensure data quality and integrity, optimize workflows, and collaborate with Operations, IT, Accounting, HR, Property Services, Marketing, and other departments. The role requires SQL Server, programming, shell scripting, cloud, containerization, version control, and data pipeline knowledge, with occasional travel and flexible scheduling as project deadlines require.

Leads the design, development, and optimization of scalable data pipelines and analytics platforms. Builds ETL/ELT workflows using Databricks and Spark, integrates and transforms source data into curated models, manages SQL Server data, and maintains documentation and lineage. Collaborates with product, analytics, and engineering teams to deliver reliable data solutions. Applies responsible AI practices and delivers projects independently within established timelines.

Build and maintain dbt data models and pipelines for billing, payments, usage, and revenue data. Ensure data accuracy, reconciliation, integrity, and timely delivery while defining metrics and monitoring systems. Develop dashboards and self-service data products, support finance data operations, and collaborate with Billing Engineering, Finance, Accounting, and Product teams. Influence billing data architecture and engineering roadmaps.

Designs, builds, and maintains scalable data pipelines and ETL processes using Databricks. Implements CI/CD and infrastructure automation with Jenkins and Terraform, develops 3NF, dimensional, and Data Vault models, optimizes performance, and ensures data quality. Collaborates with data scientists, analysts, and software engineers, documents solutions, establishes best practices, and mentors junior team members.

Design, build, and optimize enterprise ETL/ELT pipelines, cloud data lakes, lakehouse solutions, data models, and semantic layers across AWS and Azure. Ensure data quality, security, performance, reliability, and governance while supporting analytics, reporting, and AI initiatives. Collaborate with product, engineering, IT, and business stakeholders to translate requirements into scalable data solutions, monitor pipeline health, troubleshoot issues, and improve data architecture and practices.