Mastercard

1 month ago

Director, Data engineering

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 20 hours agoStatus: Live

Job Summary

Lead the strategy, architecture, and delivery of Mastercard’s data platform powering analytics and agentic AI applications. Own data foundations for multi-agent systems, ensure real-time retrievability, governance, and security at production scale, and collaborate with Applied AI, Product, and Architecture leadership.

Location: Dublin, Mountain View

Workplace: Onsite

Employment Type: Full time

Job Function: Executive & General Management

Seniority: Director level

Key Responsibilities

•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 AI-related requirements (PII handling, prompt/response logging, audit trails).
•Set technical standards for exposing data products to agents and applications, including MCP patterns, schema contracts, and tool interfaces.
•Partner with Applied AI on evaluation/runtime data needs — training sets, eval datasets, retrieval quality, and feedback loops.
•Stay hands-on 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.

Key Requirements

•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).
•Strong hands-on background in data architecture, modelling, streaming, and lakehouse design on AWS.
•Proven ability to take data systems from concept to secure, scalable production.
•Excellent technical communication and ability to align engineers, AI scientists, product managers, and executives.

Experience:AIData platformDistributed data processingCloud AWSDatabricksSparkKafka

Skills:LeadershipCommunicationStakeholder managementMentorshipProblem-solving

Tech Stack:SparkDatabricksKafkaDbtDeltaIcebergAWSMCPVector storesFeature storesKnowledge graphsLLM

Company Brief

Mastercard

Mastercard is a global payments and technology company that processes transactions, provides fraud prevention and data services, and builds payment infrastructure for consumers, businesses, merchants and governments across 200+ countries.

Industry: Payments

Company Size: Enterprise (1,001+ employees)

Revenue: USD 1B+

Growth: Public Company

Valuation: Public Company (Market Cap in USD)

Funding: IPO / Publicly Listed

Headquarters: Purchase, New York, United States

Founded: 1966

Glassdoor: 4.1

Website LinkedIn Glassdoor

Loading job details...

Preparing the role view and application actions.

Mastercard

1 month ago

Director, Data engineering

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 20 hours agoStatus: Live

Job Summary

Location: Dublin, Mountain View

Workplace: Onsite

Employment Type: Full time

Job Function: Executive & General Management

Seniority: Director level

Key Responsibilities

•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 AI-related requirements (PII handling, prompt/response logging, audit trails).
•Set technical standards for exposing data products to agents and applications, including MCP patterns, schema contracts, and tool interfaces.
•Partner with Applied AI on evaluation/runtime data needs — training sets, eval datasets, retrieval quality, and feedback loops.
•Stay hands-on 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.

Key Requirements

•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).
•Strong hands-on background in data architecture, modelling, streaming, and lakehouse design on AWS.
•Proven ability to take data systems from concept to secure, scalable production.
•Excellent technical communication and ability to align engineers, AI scientists, product managers, and executives.

Experience:AIData platformDistributed data processingCloud AWSDatabricksSparkKafka

Skills:LeadershipCommunicationStakeholder managementMentorshipProblem-solving

Tech Stack:SparkDatabricksKafkaDbtDeltaIcebergAWSMCPVector storesFeature storesKnowledge graphsLLM

Company Brief

Mastercard

Industry: Payments

Company Size: Enterprise (1,001+ employees)

Revenue: USD 1B+

Growth: Public Company

Valuation: Public Company (Market Cap in USD)

Funding: IPO / Publicly Listed

Headquarters: Purchase, New York, United States

Founded: 1966

Glassdoor: 4.1

Website LinkedIn Glassdoor