Lead Data Platform Engineer

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   Lead Data Platform Engineer
Overview   The Mastercard Economics Institute (MEI) is an economics lab powering scale at Mastercard by owning economic thought leadership in support of Mastercard’s efforts to build a more inclusive and sustainable digital economy
MEI was launched in 2020 to analyze economic trends through the lens of the consumer to deliver tailored and actionable insights on economic issues for customers, partners, and policymakers
The Institute is composed of a team of economists and data scientists that utilize & synthesize the anonymized and aggregated data from the Mastercard network together with public data to bring powerful insights to life, in the form of 1:1 presentation, global thought leadership, media participation, and commercial work through the company’s product suites
About the Role   Mastercard Economics Institute (MEI) is seeking an experienced Lead Data Platform Engineer to lead our multiyear data strategy and own day to day data operations for a modern, research grade analytics environment. Reporting to MEI’s VP for Applications & Innovation, you will ensure MEI’s data is well managed, well structured, well governed, documented, and reliably accessible to economists and analysts.
This is a unique opportunity for someone energized by managing large scale datasets across relational and distributed systems, automating data pipelines, and adopting new applications and platforms—while applying AI to improve data management and unlock differentiated insight. This role ensures the integrity and accessibility of the data that underpins strategically important priorities to deliver insights, support leadership, and engage customers with precision and speed.
Responsibilities     Develop MEI’s multiyear data strategy aligned to business objectives in Economics, Product, and Customer Engagement.
Liaise with Mastercard Technology (Biz Ops, Data Platform, DB Admins, Security, Networking) to align on access to centralized datasets and data access technology, standards, timelines, and support models.
Standardize data and platforms orchestration and usage. Maintain compliance with data security, privacy, and regulatory requirements.
Own day to day operations for MEI data environments (Hadoop/Ozone/Cloudera; SQL; R/Python; Tableau) and Databricks (lakehouse architecture, Spark jobs, Splunk Catalog, Delta, etc.). Design, build, and run ETL/ELT pipelines for automated data refresh across internal and external datasets.
Define and implement data quality standards (accuracy, completeness, timeliness, consistency, uniqueness) with automated checks, alerts, and remediation workflows.
Create templates, starter kits, and documentation to reduce onboarding friction and enable independence.
All About You     Bachelor’s in Computer Science, Information Systems, Data Science, Engineering, or related (Master’s preferred), or equivalent experience.
Years of professional experience in data engineering/platform operations
Hands on with Hadoop/Ozone/Cloudera, Spark, SQL (OLAP/warehouse), Python/R, and BI/Tableau.
Expertise in ETL/ELT orchestration, CI/CD for data, and environment management (dev → test → prod).
Proven track record migrating to Databricks or building lakehouse architecture is required.
Strong background using Cloud platforms (AWS/Azure/GCP) and enterprise security/compliance frameworks.
Orchestration tools (Airflow/Databricks Jobs), dbt, and (optionally) streaming (Kafka).

Experience with GenAI/LLMs for data & analytics (e.g., RAG, vector search, NL→SQL), and basic Lang Ops/MLOps (MLflow/Model Registry).
Strong data governance & quality: catalog/lineage, data contracts,…