Top Data & Analytics Jobs

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, 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.

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.

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.

Build and operate batch and streaming data pipelines, ETL/ELT workflows, data warehouses, dimensional models, and reusable data services. Improve reliability, performance, quality, and cost efficiency through monitoring, testing, automation, and incident response. Partner with analytics, data science, product, business, platform, and infrastructure teams to deliver production data solutions and roadmap initiatives. Apply AI-assisted tools responsibly to development, testing, documentation, and troubleshooting.

Administer Tableau Server for 700 users across 12 department sites, managing upgrades, patches, licenses, SSO, data drivers, access, troubleshooting, and support requests. Build decision-ready dashboards and data products, maintain report inventories, guide migration or retirement decisions, and establish standards for data access, row-level security, and visualization quality. Partner with business stakeholders and developers while using AI tools to accelerate analysis, development, and migrations.

Build and maintain batch and streaming data pipelines supporting enterprise analytics and AI. Develop transformations, analytical data models, quality checks, integrations, and reliable production workflows. Monitor and troubleshoot pipeline issues, support data-source onboarding, optimize platform performance and costs, and follow governance and security standards. Collaborate with BI developers, analysts, and data scientists while writing tested, documented code in an Agile environment.

Own Modal’s go-to-market data platform, including organization and people graphs, identity resolution, CRM schemas, Snowflake and dbt models, reverse ETL pipelines, revenue operations metrics, forecasting, attribution, and data quality systems. Partner with Data, RevOps, and Marketing to support customer context, routing, territory management, campaigns, reporting, and reliable GTM workflows.

Build scalable data ingestion, transformation, entity resolution, and integration pipelines for a cybersecurity ontology and knowledge graph platform. Design batch and real-time distributed systems processing billions of security events, ensure data quality and governance, model entities and relationships, and enable AI agents through structured security knowledge and ontology APIs.

Designs, builds, deploys, and operates scalable data pipelines and cloud-based data architectures supporting lending analytics across Mortgage and Personal Loans. Responsibilities include data ingestion and transformation, schema design, SQL optimization, workflow orchestration, monitoring, troubleshooting, data quality, and generative AI integration. The role also establishes engineering standards and collaborates with analysts, engineers, and governance teams to deliver reliable production data systems.

Build and maintain Python-focused data analytics applications, pipelines, integrations, batch workflows, and data-quality solutions for Prime Services financial data. Partner with quantitative analysts and business stakeholders to translate requirements into production-grade systems. Support global production operations, monitoring, incident resolution, and data governance while using technologies such as Kafka, Spark, S3, Trino, and Starburst. Contribute to AI-powered monitoring agents and scalable analytics platforms.

Designs and maintains enterprise healthcare EDI data pipelines for HIPAA X12 transactions, Medicaid encounters, and CMS compliance. Builds ETL/ELT workflows using Databricks, PySpark, Delta Lake, and SQL Server; develops validation, reconciliation, monitoring, and alerting frameworks; troubleshoots clearinghouse integrations; supports regulatory updates and encounter submissions; and collaborates with claims, compliance, security, product, and external healthcare teams.

Designs, builds, and operates governed AWS data lake infrastructure, self-service platform tooling, ETL/ELT pipelines, and data governance capabilities. Develops Python services supporting GenAI, RAG, and knowledge-based applications; contributes to streaming pipelines and large-scale PySpark processing. The role also manages infrastructure as code, observability, data quality, cost optimization, and platform reliability while partnering with engineering, data science, and business teams.

Own datasets for attribution, campaign performance, and customer lifecycle analysis. Design real-time and batch data pipelines, build scalable analytics infrastructure, develop attribution frameworks, optimize Oracle databases and SQL data models, and improve data governance. Partner with analytics and product teams to translate business needs into data solutions.

Build and lead the telemetry and analytics data platform, including collection, transformation, aggregation, data modeling, dashboards, and self-service tooling. Process OpenTelemetry traces, metrics, and logs at scale; analyze system performance, stability, and feature usage; ensure data quality and access; and partner with frontend and backend teams on instrumentation standards. Manage two data engineers, provide technical direction, document tooling, support users, and communicate findings through visualization.

Build and support production-grade Microsoft Fabric data products and pipelines across ingestion, transformation, lakehouse, warehouse, and curated layers. Implement reliable, restartable, and observable data solutions using medallion architecture, automated testing, governance, lineage, monitoring, CI/CD, and DataOps practices. Collaborate with architects, analytics, governance, application, and partner teams while documenting mappings, runbooks, controls, and operational procedures.