Top Data & Analytics Jobs

Designs, develops, and maintains Tableau dashboards, scorecards, and reporting solutions for compliance risk monitoring, control testing, regulatory reporting, and executive decision-making. Analyzes complex datasets, builds data models, validates data quality, connects multiple data sources, automates recurring reports, and supports audits and regulatory examinations. Collaborates with risk, audit, technology, and business teams while developing knowledge of banking products, source systems, regulations, and compliance frameworks.

Builds and optimizes scalable data platforms, batch and streaming pipelines, data lakes, warehouses, models, and integration solutions. Leads technical designs, ensures security and governance compliance, owns end-to-end SDLC delivery, troubleshoots production issues, and mentors engineers. Collaborates with engineering, product, analytics, and business stakeholders to deliver reliable data products supporting analytics, machine learning, and business insights.

Design and build scalable financial data pipelines, data-serving platforms, and AI-enabling frameworks. Ensure data integrity and quality across sources, optimize query performance, troubleshoot data issues, and promote AI-assisted software development. The role requires expertise in Python or Java, databases, warehouses, ETL, batch processing, cloud infrastructure, and Kubernetes, with preferred experience in Snowflake, Airflow, and dbt.

Design and build scalable data pipelines, data-serving platforms, and AI foundations for financial data. Ensure data integrity and quality, optimize queries, troubleshoot data issues, and promote AI-assisted software development. The role requires strong software engineering, batch processing, cloud infrastructure, database, and data warehouse experience, along with effective communication and creative problem-solving.

Build and maintain scalable data pipelines, ensuring critical data is moved, transformed, stored, and available for high-priority national security users. Collaborate with software engineers and data scientists on AI and machine learning applications. Required skills include Python, Databricks, ETL development, relational databases, and Agile Scrum. The role is hybrid but requires onsite work three to five days per week and an active Secret clearance.

Designs, builds, and maintains university data pipelines, integrations, data models, and analytics-ready datasets. Transforms operational data into trusted, governed assets; implements data quality checks, documentation, security, and access controls; and supports reporting, self-service analytics, AI-ready workflows, and institutional decision-making. Troubleshoots platform issues, automates manual processes, and partners with technical and university stakeholders to improve data delivery.

Designs, develops, and maintains scalable Azure data pipelines and ETL/ELT solutions using Python, Scala, Spark, and Azure data services. Responsibilities include optimizing distributed processing, managing data quality and validation, monitoring and troubleshooting pipelines, supporting production deployments, and collaborating with architects, analysts, and cross-functional teams. The role also requires Hadoop experience, Control-M workflow orchestration, SQL performance tuning, and adherence to testing, version control, and CI/CD practices.

The FSR Engineer supports a government data platform onsite in Washington, DC. Responsibilities include configuring and troubleshooting data feeds, developing Spark transformations and datasets, monitoring builds, designing platform applications, testing with subject matter experts, responding to data inquiries, and collaborating with product teams to debug platform code.

Builds and supports data feeds, transformations, datasets, and platform applications using Spark and programming languages. Troubleshoots missing data, build failures, and application issues; monitors development progress; responds to data-footprint inquiries; and collaborates with technical teams and subject-matter experts in an onsite government-contract environment.

Leads technical projects and develops, enhances, and supports business applications, databases, workflows, integrations, and reporting solutions. Partners with stakeholders to translate business requirements into technology solutions, manages project scope and delivery, supports testing and deployment, troubleshoots complex systems, documents processes, and advances automation and application modernization. Provides technical guidance while ensuring compliance with cybersecurity, governance, architectural, and operational standards.

Designs, develops, tests, and supports enterprise data integration solutions using Snowflake and AWS. Responsibilities include data architecture, dimensional modeling, SQL and PL/SQL development, pipeline performance tuning, data quality, risk management, incident resolution, stakeholder collaboration, Agile delivery, testing, and user training. The role also contributes to backlog prioritization, system design, technical support, and employee development.

Designs, develops, and maintains enterprise ETL/ELT pipelines using Python and SQL. Integrates and transforms data from multiple financial systems into standardized, governed datasets for reporting, analytics, and business applications. Responsibilities include data modeling, lineage documentation, quality validation, reconciliation, troubleshooting, performance optimization, and collaboration with technical and functional stakeholders. The role requires supporting evolving business rules and enterprise data sources while maintaining reliable, traceable, production-ready data assets.

Build and maintain Google Cloud data pipelines and ETL/ELT processes supporting GenAI and RAG solutions. Classify and govern sensitive data using Dataplex and Cloud DLP, enforce access controls, assess AI readiness, and design reusable schemas and data models. Collaborate with engineering, delivery, and client teams to integrate legacy systems, monitor pipeline reliability, conduct data quality testing, document data flows, and proactively identify compliance and delivery risks.

Designs and maintains scalable Azure data ingestion pipelines, integrates Azure AI services for intelligent enrichment, optimizes ETL/ELT workflows, and automates monitoring and alerting. Collaborates with data scientists to produce AI-ready datasets and contributes to architecture, governance, security, and compliance practices. The role also supports front-end UI/UX, graph-based architectures, containerization, and cloud deployment for government analytics and AI services.

Build and maintain scalable data pipelines in Databricks using PySpark, Python, and SQL. Analyze large datasets, develop dashboards and reports, resolve data quality issues, optimize data models, and support governed analytics assets. Collaborate with federal stakeholders, architects, data scientists, and engineers while documenting solutions and participating in Agile delivery.

Develop, deploy, maintain, and optimize data solutions, including complex SQL queries, stored procedures, ETL/ELT pipelines, and data models. Analyze and refactor legacy processes, perform QA and testing, collaborate across teams, document solutions, and work within Agile environments. The role also supports CI/CD practices and may involve cloud platforms, scalable web data solutions, and business intelligence tools.

Designs, builds, and maintains scalable ETL and data processing pipelines supporting analytics, forecasting, risk, and customer insights. Leads complex SQL development, data modeling, governance, quality, lineage, security, and compliance. Partners with engineering, finance, sales, compliance, product, analysts, and data scientists to translate requirements into reliable workflows. Evaluates emerging technologies, leads proof-of-concepts, and develops automated solutions using Python, Spark, Databricks, cloud platforms, orchestration tools, and modern data warehouses.

Designs, develops, and maintains secure enterprise data pipelines, integrations, models, and curated datasets across cloud and on-premises platforms. Ensures data quality, governance, security, monitoring, and performance while troubleshooting production issues and supporting migrations and modernization. Collaborates with analytics, cybersecurity, infrastructure, application, and mission teams in an Agile/DevOps environment. Produces technical documentation and supports mission-critical services, including incident response and after-hours operations.

Build and maintain ETL programs, data pipelines, entity-resolution solutions, enterprise databases, and data models for Federal Law Enforcement investigations. Develop complex SQL queries, tune database performance, integrate disparate data sources, and deliver actionable results. Collaborate directly with government customers, follow SDLC and version-control practices, handle sensitive data, and support software development, R&D, and proof-of-concept initiatives. This is a full-time, on-site role in Burlington, Vermont, requiring U.S. citizenship and eligibility for government clearance or suitability determination.

Designs, builds, and maintains batch and streaming data pipelines supporting analytics and reporting. Responsibilities include data modeling, source integration, quality monitoring, performance optimization, governance, access control, documentation, and collaboration with analysts, data scientists, and software engineers. The role requires cloud data platform experience, orchestration skills, SQL, programming knowledge, and familiarity with Azure infrastructure, security, CI/CD, and Microsoft Fabric.

Designs and maintains CORE’s enterprise analytics data platform, including dimensional data warehouses, ETL/ELT pipelines, semantic models, and governed datasets. Integrates clinical, financial, workforce, donor, and operational data while ensuring platform security, reliability, performance, quality, lineage, documentation, and monitoring. Supports Power BI, advanced analytics, and responsible AI initiatives, collaborates with technical and business stakeholders, and translates requirements into sustainable production solutions.

Designs and maintains real-time and near-real-time data ingestion pipelines using Apache NiFi, REST APIs, scripting, and streaming technologies. Ensures data quality, observability, scalability, security, and compliance when transferring data across security domains. Responsibilities include parsing and transforming structured and unstructured data, monitoring pipeline health, automating validation and alerts, documenting workflows, and collaborating with engineering, analytics, platform, security, and governance teams. The role is on-site in Honolulu and requires U.S. citizenship and an active TS/SCI clearance.

Build and operate scalable cloud data pipelines, warehouses, transformation layers, and ML production systems. Lead data infrastructure architecture, warehouse performance optimization, DataOps monitoring, CI/CD improvements, data quality testing, and large-scale data product delivery. Collaborate across teams, promote data stewardship, and support batch and streaming pipelines while deploying, serving, and monitoring machine learning models.

Administers and supports Azure data platforms, troubleshooting databases, ETL pipelines, large datasets, data-quality issues, and performance bottlenecks. Develops and supports data workflows using Python, Databricks, PySpark, T-SQL, and related Azure technologies. Responsibilities include query optimization, stored procedures, indexing, root-cause analysis, incident ownership, stakeholder communication, production support, and technical coaching.

Leads the design, development, optimization, and migration of modern data platforms in Snowflake. Builds pipelines with dbt, Python, and cloud services; gathers client requirements; creates solution designs and project plans; advises on Snowflake performance, cost, security, and workload management; integrates BI, governance, and AI/ML tools; contributes to accelerators and mentors junior engineers. The role is remote, client-facing, consultative, and focused primarily on technical delivery rather than sales.