Our Purpose Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we're helping build a sustainable economy where everyone can prosper. We support a wide range of digital payments choices, making transactions secure, simple, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential. Title and Summary Director, Data engineering Who is Mastercard? Mastercard is a global technology company in the payments industry. Our mission is to connect and power an inclusive, digital economy that benefits everyone, everywhere by making transactions safe, simple, smart, and accessible. Using secure data and networks, partnerships and passion, our innovations and solutions help individuals, financial institutions, governments, and businesses realise their greatest potential. Our decency quotient, or DQ, drives our culture and everything we do inside and outside of our company. With connections across more than 210 countries and territories, we are building a sustainable world that unlocks priceless possibilities for all. Overview The CNPF Data & AI organisation is looking for a Director of Data Engineering to lead the strategy, architecture, and delivery of the data platform powering our analytics products and agentic AI applications across Small & Medium Enterprise (SME), Corporate Solution, Transfer Solution and Commercial Verticals. This is a senior, hands-on technical leadership role within Data & AI Product Enablement. The Director will own the data backbone that our LLM agents, MCP servers, and analytics products run on - making sure data is reliable, governed, retrievable in real time, and ready for AI consumption at production scale. The role works in close partnership with Applied AI, Product, and Architecture leadership. Role Own the data engineering strategy and technical direction for CNPF, with a strong focus on enabling agentic AI and GenAI products in production Architect and deliver the data foundations for multi-agent systems - including MCP servers exposing data and tools to agents, retrieval pipelines, vector stores, feature stores, and knowledge graphs Lead the design of context-engineering infrastructure that lets agents reason over Mastercard data safely, with the right grounding, freshness, and access controls Drive lakehouse, streaming, and event-driven platform design (Databricks, Spark, Kafka, Delta/Iceberg) to support both batch analytics and low-latency AI use cases Ensure data systems meet Mastercard standards for governance, lineage, data quality, observability, and risk - including the additional requirements that come with AI consumption (PII handling, prompt/response logging, audit trails) Set technical standards for how data products are exposed to agents and applications, including MCP design patterns, schema contracts, and tool interfaces Partner with Applied AI on evaluation and runtime data needs - training sets, eval datasets, retrieval quality, and feedback loops Stay hands-on enough to make sharp architectural calls, review designs, and unblock the team on hard problems Guide a team of senior data engineers, providing technical direction and growing their capability over time ALL ABOUT YOU Significant experience leading the design and delivery of large-scale data platforms in production Deep expertise in distributed data processing and the modern data stack - Spark, Databricks, Kafka, dbt, Delta/Iceberg, and similar Strong hands-on background in data architecture, modelling, streaming, and lakehouse design on AWS Proven track record of taking data systems from concept to secure, scalable production Solid grasp of data governance, lineage, quality, and observability frameworks Excellent technical communication - able to align engineers, AI scientists, product managers, and executives Comfortable operating as a player-coach: setting direction, reviewing designs, and going deep when needed What Makes You Stand Out You have personally built data infrastructure that powers agentic AI in production - not just analytics dashboards Hands-on experience designing and operating MCP (Model Context Protocol) servers, including authentication, tool exposure, schema design, and observability Direct experience building the data layer for multi-agent systems - retrieval, memory, state management, long-running workflow data, and human-in-the-loop checkpoints Strong familiarity with vector databases, hybrid retrieval (semantic + structured), and knowledge graph integration with LLMs Practical understanding of LLMOps data needs - eval datasets, golden traces, prompt/response telemetry, and feedback capture Experience designing real-time and event-driven systems that support low-latency agent decisioning Sharp instincts for the trade-offs between batch and streaming, structured and unstructured, accuracy and cost - and how those decisions cascade into agent behaviour Experience partnering with security and governance teams to ship AI-facing data products responsibly at enterprise scale Corporate Security Responsibility Every person working for, or on behalf of, Mastercard is responsible for information security. All activities involving access to Mastercard assets, information, and networks come with inherent risk and therefore it is expected that the successful candidate will: Abide by Mastercard's security policies and practices Ensure the confidentiality and integrity of the information being accessed Report any suspected information security violation or breach Complete all mandatory security trainings in accordance with Mastercard's guidelines Corporate Security Responsibility All activities involving access to Mastercard assets, information, and networks comes with an inherent risk to the organization and, therefore, it is expected that every person working for, or on behalf of, Mastercard is responsible for information security and must: - Abide by Mastercard's security policies and practices; - Ensure the confidentiality and integrity of the information being accessed; - Report any suspected information security violation or breach, and - Complete all periodic mandatory security trainings in accordance with Mastercard's guidelines.

Role Description More than 12+ years of IT experience. - Microsoft Fabric (OneLake, Lakehouse, Data Factory, Power BI) - Databricks (data engineering, SQL, notebooks, app/backend integration patterns) - Enterprise data warehouses and lakehouse platforms (Snowflake, Synapse, BigQuery – experience with one or more) - Deep knowledge of data modeling, data warehousing, and lakehouse patterns - Hands‑on experience with at least one modern data platform: Microsoft Fabric and/or Databricks strongly preferred - Experience integrating data platforms with applications using APIs, services, or event‑driven patterns - Solid understanding of cloud architecture concepts (security, networking, scalability, cost management) - Strong communication skills with the ability to engage both technical and business stakeholders - Experience working in client‑facing or consulting environments Qualifications - Bachelor's Degree in Computer Science, Information Technology, Engineering, Business, or related field AND 4+ years experience in cloud/infrastructure technologies, information technology (IT) consulting/support, systems administration, network operations, software development/support, technology solutions, practice development, architecture, and/or consulting, or equivalent experience. Requirements - Data Architect - Data warehouse - Lakehouse (Databricks or Microsoft Fabric) - Datalake - ADF/Synapse/Data Pipelines (ETL/ELT) - Strong SQL and Pyspark knowledge - CI/CD Experience Company Description Datavail’s Data Management and Analytics practice is made up of experts who provide a variety of data services including initial consulting and development, designing and building complete data systems, as well as ongoing support and management of database, data warehouse, data lake, data integration, and virtualization and reporting environments. - Datavail’s team is comprised of not just excellent BI & analytics consultants, but great people as well. - Datavail’s data intelligence consultants are experienced, knowledgeable and certified in the best in breed BI and analytics software applications and technologies. - We ascertain your business objectives, goals and requirements, assess your environment, and recommend the tools which best fit your unique situation. - Our proven methodology can help your project succeed, regardless of stage. - With the combination of a proven delivery model and top-notch experience ensures that Datavail will remain the Data Management experts on demand you desire. - Datavail’s flexible and client-focused services always add value to your organization.

Role Description CTG is seeking a highly skilled Healthcare Data Engineer to support enterprise healthcare data initiatives. This role will focus on designing and building scalable cloud-based data pipelines, integrating large healthcare datasets, and enabling advanced analytics using modern data engineering technologies. The ideal candidate will have strong experience with cloud platforms, big data processing frameworks, and healthcare data environments. Location: Remote Duration: 6 Months Key Responsibilities - Design, develop, and optimize enterprise-scale ETL/ELT pipelines. - Build and maintain cloud-native data solutions using AWS and/or GCP. - Develop data processing applications using Python, PySpark, and Scala. - Integrate and transform data across Teradata, Snowflake, and other enterprise systems. - Implement scalable data architectures supporting healthcare analytics and reporting. - Deploy and manage containerized data workloads using Kubernetes. - Ensure data quality, governance, security, and compliance with healthcare regulations. - Collaborate with architects, analysts, and business stakeholders to deliver data-driven solutions. - Troubleshoot and optimize performance of large-scale data platforms. Qualifications - Strong experience with AWS and/or Google Cloud Platform (GCP). - Advanced ETL/ELT development expertise. - Hands-on experience with Teradata and Snowflake. - Strong programming skills in Python, PySpark, and Scala. - Unix/Linux administration and scripting experience. - Experience working with Kubernetes and containerized environments. - Understanding of healthcare data ecosystems and industry regulations. - Strong analytical, problem-solving, and communication skills. Requirements - 7+ years of Data Engineering experience. - Experience supporting healthcare organizations, payers, providers, or health technology companies. - Familiarity with claims, clinical, member, provider, or population health data. - Experience building large-scale cloud data platforms and distributed data processing solutions. - Knowledge of HIPAA compliance and healthcare data governance practices. Education - Bachelor's degree in Computer Science, Information Systems, Data Engineering, Healthcare Informatics, or a related field. - Advanced degree and relevant cloud certifications are a plus. - Excellent verbal and written English communication skills and the ability to interact professionally with a diverse group are required. Benefits - The expected base salary for this position ranges from $185,000 to $195,000. - Salary offers are based on a wide range of factors including relevant skills, training, experience, education, market factors, and where applicable, licensure or certifications obtained. - In addition to salary, a competitive benefit package is also offered. To Apply To be considered, please apply directly to this requisition using the link provided. Kindly forward this to any other interested parties. Thank you!

• Own our canonical data architecture — the schema, contracts, tenancy, and governance. • Make production data AI-ready: well-modeled, contract-enforced, lineage-tracked, and drift-detectable. • Own the canonical data model — the normalized definition of the core business objects shared across our products. • Define the multi-tenant data architecture: tenancy isolation, data residency posture, and per-tenant cost attribution. • Lead staged modernization toward the right mix of stores and patterns. • Own the architectural direction of the data pipeline and lake / lakehouse layer. • Drive hands-on prototypes, reference implementations, and in-repo guardrails. • Partner with database engineering on production data health while owning long-term architectural direction.