Apply on Employer Site

Mastercard · 2 months ago

Vice President, Data Platform Engineering

Arlington, Virginia

Full-time

Hybrid

Senior Level

$230K/yr - $368K/yr

Mastercard is a global leader in digital payments, seeking a Vice President of Data Platform Engineering to provide strategic leadership and operational oversight for its enterprise-wide Data Platform. The role involves modernizing the data ecosystem, driving innovation, and leading a Multi-Agent ETL Platform team to enhance data capabilities and efficiency.

FinanceFinancial ServicesInformation TechnologyPayments

Growth Opportunities

No H1B

U.S. Citizen Only

Responsibilities

Drive modernization from legacy and on-prem systems to modern, cloud-native, and hybrid data platforms

Architect and lead the development of a Multi-Agent ETL Platform for batch and event streaming, integrating AI agents to autonomously manage ETL tasks such as data discovery, schema mapping, and error resolution

Define and implement data ingestion, transformation, and delivery pipelines using scalable frameworks (e.g., Apache Airflow, Nifi, dbt, Spark, Kafka, or Dagster)

Leverage LLMs, and agent frameworks (e.g., LangChain, CrewAI, AutoGen) to automate pipeline management and monitoring

Ensure robust data governance, cataloging, versioning, and lineage tracking across the ETL platform

Define project roadmaps, KPIs, and performance metrics for platform efficiency and data reliability

Establish and enforce best practices in data quality, CI/CD for data pipelines, and observability

Collaborate closely with cross-functional teams (Data Science, Analytics, and Application Development) to understand requirements and deliver efficient data ingestion and processing workflows

Establish and enforce best practices, automation standards, and monitoring frameworks to ensure the platform’s reliability, scalability, and security

Build relationships and communicate effectively with internal and external stakeholders, including senior executives, to influence data-driven strategies and decisions

Continuously engage and improve teams’ performance by conducting recurring meetings, knowing your people, managing career development, and understanding who is at risk

Oversee deployment, monitoring, and scaling of ETL and agent workloads across multi cloud environments

Continuously improve platform performance, cost efficiency, and automation maturity

Qualification

Data engineeringAI-driven automationETL platform developmentApache SparkData governancePythonSQLCommunication skillsLeadership skillsProject management

Required

Hands-on experience in data engineering, data platform strategy, or a related technical domain

Proven experience leading global data engineering or platform engineering teams

Proven experience in building and modernizing distributed data platforms using technologies such as Apache Spark, Kafka, Flink, NiFi, and Cloudera/Hadoop

Strong experience with one or more of data pipeline tools (Nifi, Airflow, dbt, Spark, Kafka, Dagster, etc.) and distributed data processing at scale

Proficiency in Python, SQL, and data ecosystems (Oracle, AWS Glue, Azure Data Factory, BigQuery, Snowflake, etc.)

Deep understanding of data modeling, metadata management, and data governance principles

Proven success in leading technical teams and managing complex, cross-functional projects

Passion for staying current in a fast-paced field with proven ability to lead innovation in a scaled organization

Excellent communication skills, with the ability to tailor technical concepts to executive, operational, and technical audiences

Expertise and ability to lead technical decision-making considering scalability, cost efficiency, stakeholder priorities, and time to market

Proven track leading high-performing teams with experience leading and coaching director level reports and experienced individual contributors

Bachelor's degree in Data Science, Computer Science, Information Technology, Business Administration, or a related field. Equivalent experience will also be considered