Top Data & Analytics Jobs

Build and support Python data applications, migrate systems to Azure, develop ETL pipelines, manage data lake resources, and design scalable hybrid cloud architectures. The role also involves implementing statistical and machine learning models, improving data science infrastructure, documenting solutions, and supporting analytics databases.

Designs, builds, and supports enterprise data products, analytics platforms, scalable pipelines, Lakehouse architectures, and integration solutions. The role integrates operational and enterprise systems, develops governed and reusable data products, supports reporting and analytics, and implements data quality, validation, monitoring, lineage, and modeling processes. Strong Databricks, SQL, Python, Azure, and Power BI expertise is required, with experience in operationally intensive environments such as manufacturing, IoT, supply chain, or logistics.

Builds and maintains enterprise data warehouse solutions, relational database schemas, ETL pipelines, and automated data processes. Responsibilities include optimizing complex SQL queries, indexing, execution plans, data modeling, governance, system support, and cross-functional delivery. Requires strong SQL, SSIS, data warehouse, and relational database expertise, with preferred experience in Azure Data Factory, ActiveBatch, and Snowflake.

Develops and maintains enterprise data warehouse solutions, relational database schemas, ETL pipelines, and automated data processes. Optimizes complex SQL queries, indexing, and execution plans for large datasets while supporting scalable data systems. Collaborates with analysts, product managers, QA, developers, and business stakeholders throughout solution design and delivery. Requires advanced SQL, data modeling, governance, SSIS, and data warehouse experience, with Azure Data Factory, ActiveBatch, and Snowflake preferred.

As a Data Engineer, you will design, build, and maintain scalable data platforms, working on mission-driven projects that analyze big data to derive insights. You will collaborate with multi-disciplinary teams in a fast-paced environment.

Lead hands-on development of scalable real-time and batch data platforms using AWS, Databricks, Snowflake, Spark, Kafka, and Cassandra. Set technical direction for a Scrum team, write production code, mentor engineers, and establish quality standards. Design data governance, lineage, profiling, and access solutions while supporting AI/ML systems and model consumers. Partner with architecture, product, modeling, and deployment teams to deliver secure, resilient, high-performance data utilities.

Architect and lead enterprise data quality, observability, and validation frameworks for lead management systems. Own data integrity across ingestion, processing, and delivery pipelines; define data contracts, governance standards, SLAs, dashboards, and alerting. Diagnose systemic issues across distributed data flows, drive scalable architectural solutions, advise senior stakeholders, and mentor data engineers. Partner cross-functionally with Engineering, Product, and Data Science to improve reliability, customer fulfillment, and marketplace performance.

Designs, implements, and maintains secure, compliant data pipelines and scalable data architectures. Builds service-based data delivery systems, automates processes, improves data quality, and supports data integration. Collaborates with architects, analysts, business stakeholders, and end users to resolve technical issues and develop requirements. Establishes data standards, governance frameworks, performance metrics, and compliance practices while monitoring and troubleshooting integration failures through an on-call rotation.

Build and maintain scalable data pipelines, ETL workflows, databases, and cloud-based data storage for digital marketing data. Ensure data quality, accuracy, and performance while preparing datasets for dashboards and reporting. Collaborate with analysts, CSMs, leadership, and cross-functional teams to address data needs, troubleshoot issues, participate in code reviews, and support scalable data management processes.

Build secure data pipelines, enterprise relational databases, ETL/ELT workflows, and integrations for advanced visual analytics. Improve performance, security, data lineage, and ingestion across structured and unstructured data environments. Develop AI and GenAI integrations using modern application frameworks, databases, containers, and Kubernetes. Support air-gapped and multi-enclave infrastructure, mentor junior engineers, and establish cross-team technical standards.

Build and maintain FinOps data pipelines, BigQuery and dbt models, cost attribution systems, forecasting and anomaly detection models, and cost-versus-revenue analytics. Identify and implement AWS and GCP savings opportunities, automate governance with Python, Terraform, and Lambda, and partner with Engineering, Finance, Product, and leadership on cloud costs, forecasting, reporting, and architecture decisions.

Build, optimize, and troubleshoot SQL solutions and scalable data pipelines using Databricks, Spark, Delta Lake, and Python. Develop data models, integrate diverse data sources, implement ETL/ELT workflows, and improve data quality through testing and validation. Collaborate with engineers, analysts, scientists, and clients to deliver secure, high-performing analytics solutions supporting federal health and regulatory missions.

Lead the design, development, deployment, and optimization of scalable Databricks data pipelines and ETL processes. Build data models using medallion architecture, integrate AWS data services, improve performance, and ensure security, governance, and compliance. Mentor analysts, engineers, developers, and data consumers while providing technical leadership in an agile environment. Troubleshoot pipeline issues through robust logging and error handling and collaborate with cross-functional teams to deliver reliable analytics platforms.

Designs, develops, maintains, and improves software applications and business processes. Responsibilities include full lifecycle development, troubleshooting, performance and reliability improvements, CI/CD collaboration, requirements analysis, technology research, package maintenance, portfolio planning, and mentoring junior engineers. The role requires independent work, cross-functional collaboration, and strong software development, documentation, and problem-solving skills.

Build and maintain scalable Python-based data pipelines and cloud-native solutions supporting U.S. Army financial-system modernization. Develop ETL/ELT workflows using AWS Glue, Lambda, S3, and CloudWatch; integrate APIs and diverse file formats; improve data reliability, performance, security, and governance; implement testing and monitoring; and collaborate with engineers and stakeholders in an Agile environment.

Lead product data engineering across architecture, data modeling, pipelines, monitoring, lineage, and data quality. Build reliable analytical datasets, support experimentation and machine learning use cases, and resolve business-critical pipeline issues. Partner with product and engineering stakeholders to translate business needs into automated data solutions. The role requires ownership of end-to-end data engineering projects, on-call participation, and development of AI-assisted monitoring and automation.

Owns PMO metrics, dashboards, and reporting infrastructure for aerospace programs. Develops Power BI dashboards, integrates data from multiple systems, standardizes KPIs, produces program reports, performs root-cause and trend analysis, and supports risk mitigation. Establishes data governance, tracks vendor performance, and serves as a liaison across engineering, manufacturing, quality, finance, and operations. Promotes self-service analytics and data-informed decision-making across the organization.

Lead product data engineering initiatives by designing data models, owning critical pipelines, building data quality and lineage monitoring, and supporting experimentation and ML-ready datasets. Translate business needs into automated data solutions, maintain pipeline SLAs through on-call support, and develop AI-assisted tools for issue detection and resolution. Collaborate with Product, Engineering, Data Science, Machine Learning, and Applied AI teams across Block’s products.

Own and build January’s foundational data systems, including end-to-end pipelines, data quality standards, legacy migrations, recovery processes, and documentation. Partner with engineers, analysts, operations, and product teams to establish trusted, scalable data infrastructure. Prioritize high-impact improvements, manage stakeholder needs, define ownership boundaries, and use AI to create increasingly self-healing systems.

Design and maintain scalable data models, batch and streaming ETL pipelines, and end-to-end data workflows supporting business intelligence, machine learning, and operations. Ensure data quality, consistency, governance, traceability, and reliable delivery across distributed systems. Partner with business teams, platform engineers, and product managers to translate requirements into documented solutions and resolve data issues. The role is based in Seattle with three days per week in the office.

Designs, develops, tests, deploys, and monitors scalable data pipelines using Azure Data Factory, Databricks, Python, SQL, and dbt. Integrates source systems with data warehouses and other platforms, automates data processes, improves infrastructure scalability, and supports data migrations during mergers and acquisitions. Ensures data security, privacy, retention, and effective enterprise data management.

Design, build, and maintain production data pipelines, perform ETL and transformations, create data models and warehouses, automate reporting, ensure data quality and security, troubleshoot workflows, collaborate with clients and teams, and produce technical documentation.

Develop and maintain scalable data stores, pipelines, and ETL/ELT workflows that transform structured and unstructured data for analytics and reporting. Build and deploy data platforms, scripts, and batch or streaming solutions in collaboration with analysts, developers, data scientists, infrastructure engineers, and other stakeholders. Support platform scaling, monitoring, operations, and client-focused mission initiatives.

Designs, builds, automates, and maintains marketing data pipelines, integrations, models, and databases. The role ingests and transforms data from advertising and marketing platforms, ensures data quality, supports analytics and reporting, and monitors pipeline performance. Responsibilities include API integrations, documentation, troubleshooting, stakeholder collaboration, automation, and applying AI and LLM tools to improve data engineering workflows.

Develop and maintain data cleansing reports, ETL pipelines, Alteryx workflows, PL/SQL processes, Power BI dashboards, and automated solutions supporting the VA FMBT program. Provide technical support to data analysts and developers, validate data quality and KPI integrity, coordinate requirements and testing with stakeholders, and maintain reporting, scorecards, and remediation metrics. The role requires federal financial management data experience, stakeholder collaboration, and the ability to obtain Public Trust clearance.