Top Data & Analytics Jobs

Design, implement, automate, and maintain large-scale enterprise ETL processes for global clients. Deliver robust data solutions, validate and test data, apply engineering best practices, participate in code reviews, and collaborate across global teams. The role also requires managing multiple priorities, providing technical oversight and quality control, and supporting secure, compliant data practices.

Leads data engineering for cyber analytics by designing pipelines, ETL processes, storage, graph databases, and data models. Partners with data scientists and technology teams to integrate ML and AI solutions, manage production deployments, and build cohesive analytical datasets. Oversees and develops a team of data engineers, manages priorities, and collaborates with technical and business stakeholders.

Lead technical design and engineering standards for scalable data platforms using Databricks, Spark, Python, PySpark, and dbt. Own architecture reviews, CI/CD, DataOps, observability, platform administration, deployment governance, migrations, and upgrades. Resolve complex platform challenges, mentor engineers, and guide code quality. Partner with stakeholders on roadmaps, prioritization, and technical trade-offs while supporting Mastercard’s cloud strategy.

Build data engineering solutions that ingest, aggregate, unify, and analyze high-volume data from diverse sources. Develop data pipelines and models using Cloudera, Databricks, AWS, Hadoop, Python, and SQL. Partner with product, data science, platform, technology, client, and sales teams to deliver actionable insights, prototypes, business intelligence, and innovative cyber, fraud, security, and digital identity products.

Leads the architecture, delivery, modernization, and operations of enterprise data platforms, warehouses, databases, and engineering solutions. Designs ETL/ELT and CDC pipelines, manages cloud and database environments, improves reliability, security, scalability, and cost efficiency, and drives migrations and strategic initiatives. Provides technical leadership, mentors engineers, coordinates cross-functional projects, manages incidents and root-cause analysis, supports capacity planning, and collaborates with security, cloud, SRE, analytics, and business teams.

Owns the end-to-end design, development, and delivery of batch and real-time data pipelines using Databricks, Spark, Python, and PySpark. Leads dbt transformation architecture, CI/CD, DataOps, observability, streaming, platform optimization, deployments, and migrations. Mentors engineers, leads technical reviews, and partners with stakeholders on requirements and delivery trade-offs. Requires strong cloud, SQL, distributed processing, and data engineering expertise.

Develop causal economic analyses using Mastercard and public datasets. Translate business questions into identification strategies, build econometric and causal machine-learning models, lead projects from data ingestion through validation, and communicate findings to clients and stakeholders. Guide best practices, prototype scalable analytics, conduct research, and apply experimentation methods across business contexts.

Develop causal inference analyses and economic models using Mastercard and public datasets. Translate business questions into empirical designs, build and validate econometric and machine learning models, lead projects from data ingestion through stakeholder communication, guide causal inference best practices, prototype scalable analytics, and collaborate with clients and internal teams. Required expertise includes Python or R, SQL, quasi-experimental methods, causal machine learning, experimentation, and clear communication of technical findings.

Develop causal economic analyses using Mastercard and public datasets to assess business decisions, market changes, events, and external shocks. Translate business questions into empirical designs, lead projects from data ingestion through model validation, prototype scalable causal inference methods, guide best practices, and communicate findings to internal and external stakeholders. The role requires econometric and causal machine-learning expertise, experimentation experience, and strong technical communication skills.

Designs, develops, and scales secure data pipelines and platforms across enterprise data warehouses, lakes, and lakehouses. Leads cloud modernization and migration from on-premises systems using Spark, Hadoop, AWS, Airflow, and database technologies. Responsibilities include architecture, coding, testing, deployment, production support, data governance, observability, incident resolution, technical leadership, mentoring, and evaluating emerging data technologies.