Top Data & Analytics Jobs

Design, build, and maintain high-throughput data processing services, focusing on streaming and batch data systems to enhance player experiences.

The Senior Data Engineer will design and maintain data pipelines, build reusable data sets, validate data integrity, and ensure user privacy and security. This role requires strong expertise in SQL and Python, and familiarity with AWS and various data technologies.

Lead design and optimization of data models and pipelines for compliance and risk; standardize metrics and documentation; build data quality, lineage, and monitoring (including AI agents for automation); manage ETL scheduling, on-call pipeline support, and collaborate with product and non-technical partners to translate business needs into automated, production-ready data solutions.

The Senior Data Engineer will design and implement scalable data models in BigQuery using dbt for analytics and reporting, ensuring data governance and optimal performance.

Build and maintain internal data application tools and frameworks for a centralized cloud data lake. Collaborate with cross-functional teams and Data Engineers to add features and maintain Go-based data pipelines. Improve platform architecture for scalability, observability, and reliability while sharing knowledge and mentoring teammates.

Lead design and operation of large-scale data pipelines and data APIs. Build Spark/PySpark workflows on Databricks, optimize job performance, manage data quality and observability, and develop MCP servers and AI-agent integrations. Mentor engineers, define standards, support production incidents and on-call rotations, and collaborate with stakeholders to deliver scalable data platform products.

Lead and mentor a data engineering team to design, build, test, and deploy cloud-based, AI-enhanced risk management data solutions. Collaborate across Agile teams and product managers, implement big-data and streaming systems, perform unit tests and code reviews, and drive technology adoption and performance tuning.

Design, develop, test, implement, and support cloud-based data solutions and pipelines. Collaborate across Agile teams on distributed systems, microservices, ML integration, streaming, NoSQL/RDBMS, and data warehousing (Redshift/Snowflake). Mentor engineers, perform unit tests and code reviews, and optimize performance.

Design, build, and maintain BI-ready datasets, dashboards, and monitoring tools for high-throughput lab operations. Implement process monitoring (SPC), ensure data quality and audit readiness, perform root-cause analysis, and partner with Data Science to deploy predictive models that improve throughput, quality, and compliance.

Design, build, and maintain scalable Snowflake data pipelines and workflows. Ingest, transform, and deliver data from EHRs, claims, APIs, and CRM systems into analytics-ready structures. Optimize Snowflake performance and costs through warehouse sizing, query tuning, data modeling, and security design. Implement orchestration, DevOps, version control, and CI/CD practices while collaborating with client architects and analysts. The role also involves explaining technical tradeoffs, supporting repeatable deployments, adapting to unfamiliar challenges, and mentoring others.

Design, build, and maintain data pipelines, ingestion and transformation workflows, SQL-based processes, data models, and cloud data infrastructure for a B2B SaaS analytics platform. Monitor pipeline performance and data quality, troubleshoot failures, document data architecture and business logic, and collaborate with engineering, product, and analytics stakeholders.

Architects and implements scalable data pipelines and enterprise data solutions spanning data lakes, warehouses, marts, and distributed systems. Designs dimensional and semantic data models, curated datasets, and Power BI reporting solutions with DAX, security, governance, and performance optimization. Establishes data lineage, metadata, stewardship, compliance, and secure access practices. Collaborates with stakeholders to translate business and reporting requirements into reusable analytics assets, while supporting AI and MLOps data pipelines across cloud and hybrid platforms.

Designs, develops, optimizes, and maintains data pipelines and Snowflake data warehouse infrastructure using Matillion. Integrates structured and unstructured sources, ensures data quality and governance, supports data modeling and business intelligence, and troubleshoots pipeline issues. Develops automation scripts and queries with Python, SQL, and PowerShell while collaborating with stakeholders and external partners. The role also supports API integrations, cloud data solutions, documentation, and emerging AI capabilities in Snowflake Cortex.

Leads enterprise data architecture and engineering initiatives, building scalable cloud data lakes, pipelines, warehouses, and integration frameworks. Designs dimensional models, ETL/ELT patterns, governance practices, and analytics solutions using Snowflake, Azure, AWS, and big data technologies. Oversees testing, QA readiness, documentation, project planning, risk management, and delivery across multiple platforms. Collaborates with business and analytics teams, advises senior leadership, resolves technical issues, and mentors junior engineers.

Build and maintain data engineering infrastructure, software systems, pipelines, and analytics tools that improve the reliability of Starlink customer hardware. Collaborate across engineering, production, testing, inventory, quality, supply chain, and customer experience teams. Gather requirements, solve complex problems, contribute to internal and customer-facing codebases, and independently deliver reliability-focused projects in a dynamic, on-site environment.

Lead DataSF’s department-facing data engineering practice, owning engagement from scoping and onboarding through delivery and support. Define service offerings, build self-service data access pathways, establish data quality and trust standards, and integrate governance into onboarding. Partner with city departments to translate data needs into scalable technical requirements, shape dbt and analytics engineering practices, and mentor engineers or analysts. The role begins as an individual contributor and may grow into team management.

Design, develop, test, and maintain ETL pipelines that integrate data from databases, APIs, and cloud sources into warehouses or lakes. Analyze schemas, implement transformations, troubleshoot data issues, collaborate with business and product owners, and deliver scalable software solutions. The role also involves optimizing pipelines as requirements and data volumes change, participating in peer review, and improving engineering processes.

Lead design and delivery of scalable, secure cloud data pipelines and products. Translate requirements into architecture, apply advanced data modeling (Data Vault 2.0/dimensional), embed data quality, observability, security, and DataOps/CI-CD practices, optimize performance and cost, mentor engineers, and enable AI/ML-ready datasets within an agile DevSecOps model.

Build and deliver custom data solutions for major clients using LiveRamp’s platform. Responsibilities include client discovery, requirements analysis, pipeline development and troubleshooting, technical consultation, project management, presentations, and coordination across business, engineering, product, and client teams. The role requires learning complex data technologies quickly, communicating technical concepts clearly, and managing multiple implementations simultaneously.

Designs and builds ETL/ELT pipelines and data architectures across hybrid big-data and traditional database environments. Develops dimensional and normalized data models, ingests structured and unstructured sources, maintains data quality and security, troubleshoots pipeline issues, and delivers analytical reports and dashboards using Cognos and Tableau. Collaborates in agile teams, manages change requests, audits data integrity, and applies testing, documentation, and development best practices.

Supports ETL pipeline development, legacy data migration, data transformation, validation, profiling, cleaning, and integration for a secure DEA modernization program. Collaborates with technical and business teams, monitors pipeline performance, troubleshoots issues, documents data flows and migration results, and participates in Agile development activities under senior engineer guidance.

Lead architecture and implementation of end-to-end data and analytics platforms for CoreAI. Design and optimize large-scale ETL/ELT pipelines, ensure data governance, quality, lineage, security, and enable self-service analytics. Collaborate with product, engineering, AI research, and business stakeholders to translate needs into roadmaps and data solutions. Mentor senior engineers and drive engineering standards, operational rigor, and scalable analytics for AI workloads.

Designs and maintains scalable ETL pipelines for structured and unstructured data, supporting DEA legacy-data migration, validation, integration, and modernization. The role improves data quality, interoperability, reliability, and cloud-platform performance while preparing data for analytics and AI/ML use cases. Responsibilities include source-to-target mapping, troubleshooting, documentation, stakeholder collaboration, and participation in Agile development.

Lead enterprise Microsoft Fabric data engineering initiatives, including architecture, ETL/ELT pipelines, data modeling, Power BI semantic models, performance optimization, security, governance, DevOps automation, AI enablement, production support, and technical team mentoring.

Leads data engineering for a functional area by defining future-state architecture, establishing technical standards, mentoring team members, developing code and procedures, fixing bugs, maintaining quality, gathering requirements, and supporting application functionality. The role serves as a subject matter expert, partners with architecture and application engineering leaders, and drives technology adoption and professional development.