SEND Continuum, Data WARE HOUSE

About the Role

The Data Engineering team builds trusted, scalable datasets and pipelines that power analytics, BI, and machine learning use cases across Mastercard. This role will help design and deliver resilient data pipelines, curated data models, and ML-ready feature datasets while partnering closely with platform engineering, data science, and BI stakeholders.

Key Responsibilities

• Design, build, and maintain scalable batch and near-real-time data pipelines.
• Develop curated datasets and data models that enable reliable analytics and ML workloads.
• Implement pipeline reliability patterns (monitoring, alerting, retries, performance tuning).
• Partner with data scientists to enable feature engineering and model operationalization.
• Ensure secure data handling aligned to enterprise governance and compliance expectations.
• Drive engineering best practices (code quality, testing, documentation, CI/CD where applicable).

Requirements & Tech Stack

• Strong proficiency in SQL and Python (or Scala) for data engineering.
• Experience building data pipelines using distributed processing (e.g., Spark).
• Solid understanding of data modeling (dimensional and/or lakehouse patterns) and data quality concepts.
• Experience working with cloud data ecosystems (AWS preferred) and object storage / lake patterns.
• Ability to troubleshoot performance and reliability issues in production pipelines.
• Strong communication skills and ability to collaborate across engineering and analytics stakeholders.

Qualifications (Nice to Have)

• Experience with Databricks or similar lakehouse platforms.
• Exposure to ML feature pipelines, model deployment patterns, or MLOps concepts.
• Experience in payments / banking / regulated environments.
• Familiarity with data governance tooling (catalog, lineage) and PII handling patterns.