Top Data & Analytics Jobs

Designs, develops, and supports scalable cloud data pipelines, transformations, data models, and reporting datasets. Partners with engineering, business intelligence, and business teams to deliver reliable, governed data solutions. Monitors and troubleshoots production processes, improves performance and reliability, contributes to data quality and documentation standards, and participates in code reviews and engineering best practices.

Develop secure, scalable ETL and data pipelines using DMExpress and modern cloud and big-data technologies. Drive automation across data ingestion, movement, and access workflows; enforce software development and ETL engineering standards; troubleshoot production incidents; and improve processes. The role requires collaboration across distributed teams, independent execution, analytical debugging, root-cause analysis, technology evaluation, and proof-of-concept development.

Designs, develops, and maintains scalable data pipelines, transformations, datasets, and curated data products for credit risk, reporting, analytics, decisioning, and machine learning. The role uses SQL, Python, Databricks, and Spark; implements data quality controls; supports production monitoring and troubleshooting; collaborates with analytics, data science, product, and engineering teams; and contributes to cloud migration and data platform modernization.

Leads enterprise data quality strategy, architecture, platforms, frameworks, observability, governance, and automation. Designs reusable quality capabilities for batch, streaming, cloud, on-premises, and SaaS environments; establishes rules, metrics, alerts, and remediation workflows; integrates quality with metadata, lineage, catalogs, MDM, and data contracts; mentors engineering teams; evaluates tools; and applies AI and machine learning to anomaly detection, root-cause analysis, and trusted AI-ready data platforms.

Build and operate a self-service data commercialization platform spanning web experiences, APIs, cloud infrastructure, lakehouse systems, and automation. Develop scalable data platforms using Python, Spark, Databricks, orchestration, Java, React, and cloud services. Design secure, reliable workflows with observability, access controls, infrastructure as code, CI/CD, and multi-cloud architecture. Collaborate on technical trade-offs and integrate distributed systems, databases, and AI-powered capabilities.

Leads marketing analytics initiatives including A/B testing, campaign and customer journey analysis, portfolio optimization, predictive modeling, segmentation, personalization, and marketing measurement. Translates complex data into actionable business insights through stakeholder engagement and clear storytelling. The role requires statistical modeling, visualization, and strong analytical communication, with financial services or payments experience preferred.

Designs, builds, and scales secure enterprise data pipelines, warehouses, lakes, and lakehouses using Spark, Java/Scala, Hadoop, and AWS. Leads batch and near-real-time processing, cloud modernization, data governance, observability, production support, and technical initiatives across teams. Partners with stakeholders to deliver scalable solutions, troubleshoots incidents, establishes engineering standards, and mentors engineers while supporting Mastercard’s global payment platforms.

Design, build, and scale secure enterprise data pipelines, warehouses, lakes, and lakehouses using distributed computing and cloud technologies. Lead cloud modernization and workload migrations, implement data governance and observability capabilities, troubleshoot production issues, and deliver batch and near-real-time processing at petabyte scale. Collaborate with technical and business stakeholders, lead end-to-end engineering initiatives, establish architecture standards, and mentor engineers.

Own end-to-end batch and real-time data pipeline delivery using Databricks, Spark, Python, PySpark, dbt, and Kafka. Design transformation architecture, CI/CD pipelines, DataOps and observability frameworks, and administer Databricks environments. Lead streaming solutions, deployments, migrations, and upgrades while optimizing performance, access, and cost. Partner with stakeholders on requirements and technical trade-offs, mentor engineers, and lead code and design reviews.

Leads Mastercard’s enterprise data engineering strategy, architecture, cloud modernization, and platform delivery. Oversees data warehouses, lakes, lakehouses, governance, real-time and batch processing, DataOps, reliability, security, and observability. Builds and develops global engineering organizations, manages strategic programs and executive stakeholders, and drives innovation in AI-enabled data platforms and automation. Ensures data platforms support analytics, AI, product development, fraud, regulatory reporting, and operational intelligence.