Top Data & Analytics Jobs

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.

Build and maintain data pipelines and platforms using Databricks, Palantir Foundry, and Maven Smart System. Integrate, transform, govern, and manage structured and unstructured data across distributed compute environments. Support modernization of DoD data ecosystems, migrate legacy ETL processes, create analytics visualizations, and collaborate with cross-functional teams on mission-driven projects involving national security and other critical applications.

Maintain and optimize AWS-based production data pipelines, build dbt transformation models, monitor system health and data accuracy, troubleshoot issues, and support security remediation. The role requires Python, SQL, cloud data warehouse, AWS, Git, CI/CD, and production data engineering experience. The engineer will collaborate with senior and broader engineering teams, document operational procedures, improve infrastructure, and use AI-assisted development tools.

Design and maintain Snowflake-based data platforms, cloud-native data lakes, and scalable ETL/ELT pipelines for asset-backed finance analytics. Orchestrate workflows with Dagster and AWS services, ensure data reconciliation and integrity, and support migration from legacy systems. Contribute to project planning, Jira backlog management, testing, cutover planning, and cross-functional go-live readiness.

Design and maintain Snowflake data pipelines and data lake architectures for asset-based finance analytics. Develop ETL/ELT workflows, validate and reconcile financial data, migrate legacy systems, and translate quantitative finance logic into scalable architectures. Support project execution through requirements breakdown, planning, testing, cutover, and go-live readiness. Collaborate across engineering, quantitative analytics, and stakeholder teams.

Build and optimize scalable ELT pipelines, ingest data from diverse sources, enhance the enterprise data warehouse, troubleshoot production issues, and support analytics initiatives. The role partners with data owners, stewards, and IT teams while promoting data governance and information management. Required capabilities include SQL, programming, cloud data platforms, business intelligence tools, relational databases, APIs, and AI concepts.

Build and maintain scalable data pipelines, transformations, data models, and cloud data architectures using Azure Data Factory, Databricks, and SQL Server. Ensure data quality, governance, security, reliability, and performance while supporting analytics and reporting. Collaborate with technical and business stakeholders, contribute to CI/CD and platform modernization, document data solutions, and troubleshoot complex data issues.