Top Data & Analytics Jobs

Develop data algorithms and systems to analyze structured and unstructured information for defense and intelligence clients. Gather, clean, parse, and interpret disparate data sources using statistical programming and scientific methods. Translate complex data into objective insights supporting military infrastructure, resource analysis, planning, and policy decisions. Collaborate with clients and team members in a fast-paced environment while maintaining required DoD clearance.

Build and maintain scalable data pipelines, models, cost allocation systems, metrics, dashboards, and analytical tools for PlayStation’s global infrastructure. Connect infrastructure, financial, engineering, and product data to analyze cost, utilization, capacity, and unit economics. Ensure data quality, testing, observability, documentation, and reproducibility while partnering across engineering, product, finance, and operations teams.

Build trusted, secure, and repeatable data pipelines, transformations, data products, retrieval layers, and semantic data assets. Assess data quality, lineage, governance, privacy, and access constraints; support AI retrieval, embeddings, vector stores, and grounding strategies. Create test and de-identified datasets, implement monitoring and quality checks, document data assets, and ensure reliable data for AI products, control-group testing, and production handoff.

Architect and own marketing data infrastructure, including Databricks pipelines, Gold Layer tables, semantic models, and ETL/ELT workflows. Build trusted data products for analytics, automation, and AI agents; translate ambiguous business needs into scalable solutions; lead cross-team initiatives; ensure reliability and data quality; mentor engineers and analysts; and develop, evaluate, and monitor production LLM and agent systems using Python and SQL.

Architect and own marketing data infrastructure, including Databricks pipelines, Gold Layer tables, semantic models, and trusted analytics foundations. Lead cross-functional data initiatives, translate ambiguous business needs into scalable solutions, ensure data quality and reliability, and mentor engineers and analysts. Build, evaluate, and monitor production LLM and agent systems, automate marketing workflows with AI, and deliver high-quality Python and SQL solutions supporting Marketing, Sales, and R&D.

Supports and modernizes enterprise data services, including SQL Server and cloud-based ETL/ELT, data warehouses, pipelines, data integration, dimensional modeling, and OLTP environments. Develops SSIS, Azure Data Factory, and Microsoft Fabric pipelines; troubleshoots production data and integration issues; optimizes SQL and stored procedures; documents data flows and lineage; and collaborates with technical and business teams on secure, compliant data solutions.

Design, develop, and maintain data pipelines, ETL processes, integrations, data infrastructure, and complex data models. Build and support data warehouses and analytics solutions using SQL, PL/SQL, Oracle, Snowflake, and cloud technologies. Collaborate with analysts, architects, developers, data scientists, and business stakeholders throughout the delivery lifecycle. Provide production support, troubleshoot issues, tune database performance, and contribute to data-driven reporting and AI tools.

Build and maintain scalable data pipelines and platforms that integrate structured and unstructured data from APIs and databases. Apply statistical analysis, data modeling, forecasting, optimization, and data visualization to support mission-driven client solutions. Collaborate with analysts, engineers, developers, and stakeholders in an agile environment, independently examining data and communicating findings. The role requires Python, R, SQL, Tableau, Power BI, a bachelor’s degree, TS/SCI clearance, and up to 20% travel.

Designs, builds, automates, and optimizes enterprise cloud data pipelines and platforms. Responsibilities include ETL/ELT, DataOps, CI/CD, data migration, data modeling, data warehousing, lakehouse architecture, database administration, performance tuning, and production support. The role uses Databricks, Azure services, PySpark, SQL, and multiple cloud platforms to deliver scalable structured and unstructured data solutions.

Develop and maintain scalable data stores, ETL/ELT workflows, data pipelines, and platforms for structured and unstructured data. Build software to retrieve, parse, process, and convert data for reporting and analytics. Collaborate with analysts, developers, and data consumers in agile environments while supporting platform scaling, monitoring, and operations. The role requires a Secret clearance and may involve cloud, distributed computing, streaming, NoSQL, and data warehousing technologies.

Build and maintain cloud-based data pipelines, data models, lakehouse layers, and analytics-ready datasets. Collaborate with stakeholders to translate requirements into technical solutions, ensure data quality and lineage, monitor pipeline performance, investigate failures, and document remediation. Support Microsoft Fabric and Azure data platforms while meeting security, governance, audit, and government ICT requirements. Contribute to reusable enterprise data assets for reporting, analytics, data sharing, and future AI-enabled use cases.

Senior data engineer responsible for building Azure and Microsoft Fabric ETL/ELT pipelines, Lakehouse and Warehouse architectures, dimensional models, and ML data pipelines. Develops PySpark, Python, and SQL transformations; manages Azure DevOps CI/CD and Bicep infrastructure; and implements security, governance, data quality, lineage, monitoring, and performance optimization. Collaborates with data science, architecture, DevOps, security, QA, and business teams while modernizing legacy SAS and file-based processes.

Build and operate the Global Card team’s data foundation, including pipelines, schemas, data marts, feature pipelines, quality monitoring, and data contracts. Lead architecture and technical direction, partner with product, analytics, and data science teams, contribute to backend services, establish engineering standards, mentor colleagues, and ensure reliable, secure, scalable data systems.

Build and operate data-intensive web products and APIs, extend high-throughput ETL pipelines and scrapers, ensure reliability/observability, design self-scaling services, participate in code review, and collaborate across product, data, and infrastructure teams to meet production SLAs.

Designs and supports modern data platforms using Microsoft Fabric and Databricks. Builds scalable ingestion, transformation, orchestration, lakehouse, semantic modeling, and analytics solutions. Translates client business requirements into technical designs, optimizes SQL and Spark workloads, ensures data quality and performance, and enables Power BI reporting. The role is client-facing, collaborative, and consulting-oriented, requiring stakeholder communication, documentation, and adherence to engineering and delivery best practices.

Design, build, and maintain scalable data pipelines, data lakes, and databases; ingest and map customer financial datasets; monitor pipeline reliability; translate business requirements into data flows and analytical insights to ensure high-quality, usable data.

Lead the rebuild and modernization of the company’s data platform by designing and implementing an Azure and Databricks lakehouse. Responsibilities include managing Azure Data Factory pipelines, developing Delta Lake architectures, creating data models and transformations with SQL and Python, establishing development standards, monitoring data quality, troubleshooting performance issues, leading technical projects, and mentoring or hiring data engineering staff.

Build and maintain ETL/ELT pipelines, ingest and transform structured and unstructured data, integrate APIs and external sources, improve data quality and monitoring, support dashboards and ML pipelines, optimize warehouse and cloud performance, and contribute to data governance and documentation.

Designs, builds, and maintains production-grade data pipelines, ETL/ELT workflows, data models, and enterprise data platforms. Integrates databases, APIs, files, and third-party systems using Microsoft Fabric and related technologies. Ensures data quality, security, governance, monitoring, performance, and operational reliability. Collaborates with stakeholders and technical teams to deliver sustainable data solutions, document architectures, troubleshoot issues, and support continuous improvement.

Own and evolve the UFL’s data platform, including Fivetran, dbt, BigQuery, Cloud Run, ETL/ELT, reverse-ETL pipelines, and data models. Maintain reliable data flows, lead migration data mapping and validation, improve architecture and data quality, and establish engineering standards. Partner with Sales, Marketing, Finance, Sponsorships, and Football Operations to deliver trusted datasets and scalable analytics foundations.

Support design, development, and maintenance of data pipelines and infrastructure. Assist with ETL/ELT workflows, data ingestion, cleaning, validation, modeling, and warehousing. Document processes and collaborate with data teams to support analytics use cases.

Build, deploy, and operate production Spark pipelines on Databricks powering analytics products. Partner with product teams on pipeline requirements, data quality, freshness, monitoring, and observability. Diagnose performance and cost issues, establish scalable pipeline standards, and support streaming, logical replication, RAG pipelines, AWS infrastructure, and platform administration as needed.

Designs and maintains Microsoft Fabric data pipelines, medallion Lakehouse architecture, data models, governance, orchestration, and quality processes. Integrates ERP, HRIS, MRP, finance, project, employee, service, and manufacturing data into reliable data products. Supports reporting, business intelligence, AI and machine learning use cases, including RAG and vector embeddings. Troubleshoots pipeline issues, manages Fabric capacity and documentation, and collaborates with analysts, data scientists, developers, DevOps teams, and business system owners.

Leads enterprise data platform architecture, engineering standards, technical decisions, and development of scalable data products. Designs batch and streaming systems, governance, observability, security, and reliability practices. Partners cross-functionally, mentors engineers, drives modernization and migrations, and establishes reusable patterns for data modeling, orchestration, quality, and platform operations.

Build and maintain Power BI dashboards, reports, data models, pipelines, ETL/ELT processes, and data warehouses. Develop advanced DAX and SQL solutions, optimize performance, implement security and governance, manage Fabric and Power BI deployments, and integrate diverse data sources. Translate business requirements into technical solutions while collaborating with end users on testing and implementation. The role also requires strong visualization, data quality, documentation, communication, and automation skills.