Top Data & Analytics Jobs

Develops and supports business intelligence and analytics solutions, including data models, datasets, dashboards, reports, and integrated analytics. Partners with business and IT teams on data warehouse and data lake design, governance, data quality, technology performance, and analytics adoption. Uses SQL and Python to extract and shape data, automates dataset monitoring, and incorporates statistical or predictive capabilities into BI solutions.

Designs, builds, deploys, and maintains scalable cloud-native data platforms and pipelines in AWS. Supports data acquisition, preparation, database architecture, cloud migration, AI/ML, data science, and business intelligence initiatives. Leads collaborative teams in evaluating data, designing solutions, and managing operational data flows. Requires TS/SCI clearance with polygraph and experience in all-source analysis and data engineering.

Designs, builds, deploys, and maintains scalable cloud-native data platforms and pipelines using AWS. Supports cloud migration, data science, AI/ML, and business intelligence initiatives by organizing structured and unstructured data. Responsibilities include database architecture, data acquisition and preparation, software development, platform modernization, analytics architecture evaluation, and cross-functional team leadership. Requires TS/SCI clearance with polygraph and substantial all-source analysis experience.

Designs and develops cloud data architectures, ingestion frameworks, data models, and analytical platforms. Advises clients on cloud migration, data warehousing, technical approaches, and implementation roadmaps. Collaborates with stakeholders through requirements gathering, workshops, whiteboarding, and project meetings while deploying end-to-end cloud analytics solutions. Requires experience with cloud platforms, big data technologies, BI tools, data warehousing methodologies, programming, SQL, version control, and Agile/DevOps practices.

Gather client requirements and architect, develop, and deploy cloud data platforms, warehouses, ETL processes, pipelines, APIs, and BI solutions. Perform advanced SQL and data modeling, support deployments and integrations, diagnose technical issues, mentor staff, contribute to innovation and practice development, and assist with project management. The role requires approximately 20% overnight travel and primarily in-person work.

Own the technical direction, architecture, roadmap, and operational health of Teamfight Tactics’ data ecosystem. Build scalable batch and near-real-time data systems, telemetry, schemas, data models, governance, observability, and reliable sources of truth. Lead cross-functional initiatives, define engineering standards, optimize costs, mentor practitioners, and partner with game, product, insights, and data science teams across feature development and live operations.

Participates in a 12-month new-graduate development program while designing, building, deploying, and operating data architectures and pipelines. Develops processes for capturing, transforming, storing, and maintaining structured and unstructured data across distributed and local systems. Applies automation, big-data, artificial intelligence, and machine-learning techniques to support data integrity, compatibility, and evolving business requirements.

Support senior engineers in building, testing, and maintaining enterprise data pipelines and integrations. Write and optimize SQL queries, implement data quality checks, logging, and documentation, and participate in source control, code reviews, sprint activities, and support response. Complete and present an assigned data engineering project while developing practical skills in databases, scripting, ETL/ELT, and cloud data engineering.

Build scalable, secure data pipelines, models, platforms, and governance systems that enable AI applications to access trusted enterprise data. Partner with analytics, marketing, and product teams to translate requirements into technical solutions, independently deliver projects through production, and support sensitive-data security and access controls. Preferred experience includes LLM architectures, cloud data platforms, infrastructure as code, regulated environments, and technical leadership.

Build and maintain recruiting analytics data infrastructure using BigQuery, dbt, Fivetran, SQL, and Python. Design dimensional models, ETL/ELT pipelines, semantic layers, governance controls, data security, monitoring, and standardized recruiting metrics. Integrate Workday, Greenhouse, and internal systems while supporting batch and real-time analytics. Partner with data scientists, engineers, recruiters, and business stakeholders to deliver trusted data products and AI-enabled workforce insights.

Build and support production data pipelines, Databricks workflows, dimensional models, semantic models, and enterprise integrations using Azure, SQL, Python, and PySpark. Own solutions from requirements through deployment, monitoring, and support. Investigate data discrepancies, improve data quality, modernize legacy systems, and assess downstream impacts on Power BI and business metrics. Partner with stakeholders across healthcare and business functions, mentor technical teammates, and establish engineering standards for testing, CI/CD, documentation, and governance.

Develop and support Palantir Foundry data ontologies, automated data integration, predictive analytics, and proactive decision-making capabilities for Aegis missile defense system engineering. Collaborate with federal engineers, Naval Officers, and technical teams to deliver missile defense capabilities. The role requires missile systems experience, engineering or technical education, active Secret clearance, and familiarity with collaboration and project-management tools.

Design, develop, and maintain scalable data pipelines, models, databases, and data infrastructure using ETL and big-data technologies. Cleanse, integrate, and validate large datasets; implement data governance; troubleshoot complex data issues; and collaborate with analysts, scientists, and stakeholders. Develop and enhance software supporting mission requirements, maintain application databases, and coordinate hardware-software integration. The role requires onsite work in Arlington, Virginia, an active Secret clearance, and U.S. citizenship.

Provides hands-on technical leadership for foundational data engineering and analytics engineering. Owns multi-year data architecture, production pipelines, dimensional models, and trusted metrics spanning cloud costs, traffic, infrastructure, and AI usage. Designs new datasets integrating cost, utilization, performance, and reliability signals; establishes standards, coaches engineers, aligns stakeholders, and drives adoption across Infrastructure, Finance, Data Science, and Product Engineering.

Build, optimize, and maintain scalable ETL pipelines and data warehousing solutions using Azure technologies. Integrate complex datasets, manage database quality and security, automate processes, document data architectures, and collaborate with data scientists, analysts, and stakeholders. The role also requires experience with SQL, programming languages, artificial intelligence, machine learning, and related analytical methods.

Own Gradial’s end-to-end data platform, including Postgres modeling, multimodal indexing and search, production pipelines, warehouse architecture, governed metrics, and self-service analytics. Establish data quality, contracts, observability, and customer lifecycle instrumentation while setting the platform roadmap and making scalable build-versus-buy decisions. The role requires hands-on technical leadership, strong architecture judgment, and collaboration across engineering, leadership, and go-to-market teams.

Own and evolve production data systems supporting healthcare, claims, billing, analytics, and reporting. Design resilient ingestion patterns, data contracts, models, schemas, and pipelines that handle incomplete, duplicated, delayed, and changing data. Improve performance, observability, failure handling, retries, backfills, and replay safety. Collaborate with engineering, product, operations, and analytics teams while making architecture decisions and documenting technical changes.

Designs and develops scalable enterprise data architectures, models, integration strategies, governance practices, and cloud data platforms. Builds optimized data pipelines for analytics, AI, and machine learning workloads, including preprocessing, feature engineering, real-time inference, and production model integration. Collaborates with architects, engineers, analysts, stakeholders, and distributed teams across client projects. The role is hybrid in Leawood, Kansas, with occasional client-site travel.

Build and manage data ingestion, integration, transformation, modeling, and quality frameworks for federal financial management and IT programs. Analyze large datasets using SQL, Python, R, PySpark, and Databricks; develop predictive models and apply machine learning techniques. Ensure data governance and quality standards, define Databricks data quality metrics, support client meetings and documentation, and contribute to consulting and business development activities.

Architects, develops, and optimizes scalable data pipelines and cloud-native platforms integrating structured and unstructured data. Leads data engineering teams, establishes engineering standards, ensures data quality, and builds secure enterprise infrastructure supporting analytics, reporting, and mission-critical decision-making. Uses Python or Scala, SQL, Kafka, NiFi, AWS, and distributed systems while collaborating with multidisciplinary teams.

Build, maintain, and improve data integrations that extract, transform, and load retailer data into standardized schemas. Collaborate with engineering, Product, and Client Solutions teams to expand the data platform, improve reliability, monitor outages, and develop new integration capabilities. Work with data pipelines, cloud infrastructure, supply chain concepts, APIs, web scraping, browser automation, and EDI.

Lead data engineering and machine learning initiatives by designing, architecting, developing, and supporting GCP data pipelines. Create information architecture documentation, estimate and plan technical work, collaborate with project teams and business analysts, and translate requirements into technical designs. Develop Python-based ELT pipelines, optimize BigQuery SQL, support CI/CD, and provide technical leadership to projects and teams.

Build, deploy, and maintain scalable data pipelines and platforms for mission-driven projects. Validate and normalize datasets, develop schemas, implement ETL workflows, and create monitoring dashboards and metrics. Collaborate with AI/ML engineers, cloud architects, and technical leaders to support governed, high-quality data flows and reliable model performance. Improve pipeline performance and support data engineering for operational, research, and enterprise systems.

Build and maintain scalable data pipelines and platforms using SQL, Java, or Python. Acquire, tag, integrate, and process structured, semi-structured, and unstructured data. Apply machine learning models for classification, use BigID and AWS, and collaborate with analysts, engineers, developers, and data consumers in an agile environment. Guide analytical exploration, data examination, platform design, deployment, and maintenance for mission-driven client projects.

Leads the architecture, development, and support of enterprise data warehouses, lakehouses, dimensional models, ETL/ELT pipelines, and governed data products using Microsoft Fabric and related tools. Ensures data quality, security, scalability, performance, and reliability for analytics and reporting. Partners with technical and business stakeholders, documents data solutions, guides architecture decisions, mentors team members, and drives continuous improvement of data engineering standards and practices.