Top Data & Analytics Jobs

Designs scalable Databricks solutions and ETL/ELT pipelines while administering MariaDB and SQL Server environments. Responsibilities include database design, performance tuning, backup and disaster recovery, security and access management, data modeling, quality and lineage, and troubleshooting complex data issues. The role collaborates with stakeholders and technical teams, translates requirements into secure solutions, provides technical direction, and delivers demonstrations and training for Department of Transportation support.

Supports secure data pipelines, ETL/ELT workflows, data transformations, and analytics-ready datasets in a classified DoD environment. Collaborates with data scientists, engineers, and mission stakeholders while using Python, SQL, Spark, Databricks, and comparable platforms. Performs data validation, modeling, quality practices, testing, documentation, and Agile development. The role is fully on-site in a SCIF at Camp Smith, Hawaii, and requires an active Top Secret/SCI clearance before starting.

Designs, builds, deploys, and maintains scalable data platforms, warehouses, lakes, ETL/ELT pipelines, and batch or streaming workflows. Optimizes cloud data lake and pipeline performance, manages data governance and lifecycle solutions, and collaborates with multidisciplinary agile teams. The role supports mission-driven client projects involving disparate structured, unstructured, and event-streaming data.

Lead the technical design and operation of consumer-facing data products on a Databricks lakehouse. Own medallion architecture, dimensional modeling, ODCS data contracts, data quality, observability, CI/CD, incident response, and production standards. Write and review Python and SQL, guide code reviews, mentor engineers, collaborate with product, platform, governance, and ML teams, and ensure compliant, reliable delivery in a regulated insurance environment.

Designs and maintains scalable batch and streaming data pipelines, data warehouses, cloud data platforms, validation frameworks, and governance processes. Supports analytics through data modeling and Tableau enablement, resolves complex data issues, delivers architecture prototypes, collaborates with business and audit stakeholders, owns projects end to end, and mentors engineers. Requires advanced SQL and Python, AWS, Snowflake or Databricks, orchestration, transformation tooling, regulatory compliance, and extensive data engineering experience.

Designs, builds, and maintains scalable ETL/ELT pipelines, cloud data infrastructure, storage, and batch or real-time processing workloads. Optimizes queries, PySpark jobs, pipelines, performance, and cloud costs. Partners with BI, analytics, data science, and AI teams to develop dimensional data marts supporting reporting and machine learning. Implements governance, data quality validation, lineage tracking, security controls, and automated infrastructure and code deployments using CI/CD practices.

Build and optimize scalable data pipeline architecture, assemble complex datasets, improve data delivery infrastructure, and support data initiatives across engineering, product, BI, and data science teams. The role requires production-scale software engineering experience focused on data and ML processes, expertise with Spark, Kafka, SQL, and a programming language such as Python, Scala, or Java, plus knowledge of databases, data modeling, and ML operations.

Provides senior technical leadership for enterprise data engineering platforms, with emphasis on Snowflake, ETL/ELT pipelines, data quality, governance, observability, reliability, and production operations. The role guides architecture and engineering standards, supports incident response and on-call operations, improves platform scalability and resilience, and partners with engineering, infrastructure, security, governance, architecture, and business teams. It is a hands-on leadership position without direct people-management responsibility.

Designs, builds, and optimizes scalable end-to-end data pipelines and curated data products using Snowflake, SQL, and Python. Translates business needs into reliable technical solutions, ensuring data quality, governance, performance, and cost efficiency. Troubleshoots complex issues, contributes to architectural decisions, uses AI-assisted engineering tools, and collaborates with engineering, analytics, product, and business stakeholders. The role also includes code reviews, documentation, mentoring, and influencing data engineering best practices.

Develop and manage data pipelines, integrations, analytics solutions, ontologies, business object models, and enterprise data architectures using Palantir Foundry. The role supports data governance, quality, lineage, operational workflows, technical solution design, and cross-functional collaboration with technical and business stakeholders. Federal or Department of Defense experience and Agile experience are preferred. A DOD Secret clearance is required.

Designs, analyzes, documents, integrates, and migrates complex environmental datasets for AirHub business lines. Responsibilities include analyzing legacy databases, creating logical and physical data models, documenting mappings and business rules, developing migration specifications and API integrations, supporting data governance and testing, and producing technical documentation. The role also requires close collaboration with business stakeholders in an Agile delivery environment.

As a Data Engineer, you will design, implement, and maintain data systems for Notifications at Whatnot, ensuring data quality and performance while collaborating with cross-functional teams on data architecture and workflows.

Senior Data Engineer responsible for designing and deploying scalable data pipelines, ensuring critical data availability, movement, transformation, and storage. The role requires Python, Databricks, ETL, relational database, and Agile Scrum experience, supporting AI and machine learning applications for national security missions. The position is primarily onsite three to five days per week and requires an active Secret clearance and U.S. citizenship.

Designs and leads cloud-native data engineering and architecture solutions for Supply Chain analytics. Responsibilities include building AWS data pipelines and lakehouse platforms, implementing Apache Iceberg and Medallion Architecture, developing Redshift data products, applying data mesh and governance principles, integrating SAP and Blue Yonder data, and leading technical engineering teams.

Architects and develops scalable big data pipelines, ETL processes, real-time analytics frameworks, and cloud data infrastructure for battery and time-series data. Ensures data quality, designs storage and data models, supports production AI/ML workflows, and integrates model outputs into pipelines. Translates business and engineering needs into functional specifications while coordinating internal and external stakeholders.

Develops scalable data models, Databricks pipelines, backend applications, ETL processes, and data ingestion systems. Partners with engineering, product, and business stakeholders on technical roadmaps; monitors and optimizes data systems for performance, reliability, and scalability. Provides technical leadership, mentors junior engineers, conducts code reviews, and builds secure solutions for sensitive healthcare data.

Build and operate Figma’s shared data platform supporting analytics, data science, and AI products. Responsibilities include improving development, testing, CI/CD, deployment, monitoring, data quality, incident response, performance, governance, lineage, and self-service data pipeline workflows. The role partners cross-functionally to establish reliable patterns for batch, micro-batch, reverse ETL, and product-facing data delivery.

Designs, builds, optimizes, and maintains data pipelines, ETL/ELT workflows, data platforms, and data models. Supports high-volume data processing, cloud and hybrid modernization, data quality, metadata, lineage, governance, and analytics-ready datasets for federal mission and AI/ML applications. Collaborates with data scientists, architects, and analysts while producing technical documentation. The role may be filled across junior, mid-level, senior, or principal career levels.

Own the architecture and technical direction of Northbeam’s data ingestion and integration platform. Design scalable API-based ETL, event-driven and batch processing systems; establish standards for observability, freshness, and failure handling; and guide build-versus-buy decisions. Collaborate across engineering, infrastructure, product, and business teams while mentoring engineers, leading design reviews, and driving complex platform initiatives to completion.

Designs and builds scalable data platforms and pipelines for scientific, clinical, manufacturing, and process-engineering data. Responsibilities include developing batch and streaming ETL/ELT systems, integrating Databricks, Snowflake, cloud platforms, MES, SCADA, and laboratory systems, and enforcing governance and GxP compliance. The role provides hands-on technical leadership, establishes data engineering standards, and collaborates with scientists, process engineers, and platform teams. It requires three days onsite weekly in Indianapolis and unrestricted U.S. work authorization.

Own the architecture, reliability, governance, and evolution of the Databricks lakehouse. Design batch and streaming pipelines, develop SQL/Python/PySpark transformations, productionize analytical and AI prototypes, and implement testing, orchestration, monitoring, data-quality controls, governance, and access management. Collaborate with analysts, Engineering, and Product to deliver scalable, cost-effective data solutions for clinical trial insights.

As the founding engineer of the Data Intelligence team, you will design semantic layers, develop AI tooling, and automate data processes to drive business insights.

Design, build, and operate scalable batch and streaming data pipelines and analytical data models. Lead data architecture across ingestion, transformation, storage, and serving layers while implementing testing, CI/CD, observability, quality, lineage, governance, and access controls. Integrate restaurant and enterprise data sources, optimize platform performance and cost, deliver production-ready datasets for BI and AI, mentor engineers, and collaborate with cross-functional technical stakeholders.

Develop and maintain data engineering tools, services, ETL pipelines, ingestion processes, data transformations, extraction tools, and enterprise data models. Analyze structured and unstructured source data, optimize query performance, process large data volumes, collaborate with data scientists, and provide project documentation.

Owns clinical data operations by building and maintaining ELT pipelines, warehouse models, data contracts, quality controls, reconciliations, dashboards, and monitoring. Integrates clinical, laboratory, sequencing, and corporate data into validated, audit-ready systems. Supports production reliability, partner data transfers, regulatory compliance, root-cause analysis, documentation, stakeholder collaboration, and technical guidance for research and development teams.