Experienced AI Engineer designing and building production - grade agentic AI systems using generative AI and large language models. Collaborating with data engineers, data scientists in a tech - driven company.
About the role
- Principal Data Engineer modernizing cloud-native platforms for AI-powered solutions at Mastercard. Leading teams to enhance data processing efficiency and reliability across global operations.
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.
Requirements
- 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.
- Experience building and managing AI-augmented or agent-driven systems will be a plus.
- 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.
- Advanced degree in Data Science, Computer Science, Information Technology, Business Administration, or a related field. Equivalent experience will also be considered.
Benefits
- insurance (including medical, prescription drug, dental, vision, disability, life insurance)
- flexible spending account and health savings account
- paid leaves (including 16 weeks of new parent leave and up to 20 days of bereavement leave)
- 80 hours of Paid Sick and Safe Time, 25 days of vacation time and 5 personal days, pro-rated based on date of hire
- 10 annual paid U.S. observed holidays
- 401k with a best-in-class company match
- deferred compensation for eligible roles
- fitness reimbursement or on-site fitness facilities
- eligibility for tuition reimbursement
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