Lead Data Engineer

Mastercard•Austin, TX

1d•$122,000 - $207,000•Onsite

About The Position

Mastercard's Customer Experience and Disputes team is transforming the post-purchase journey by reducing friction, improving transparency, and enabling real-time collaboration between consumers, merchants, and issuers. This role will contribute to shaping innovative solutions that simplify transaction recognition, enhance purchase management, and streamline dispute resolution. We are seeking a highly analytical, execution focused, and collaborative Lead Data Engineer to join our Analytics team. This role partners closely with analysts, data engineering teams, and business stakeholders to deliver end to end analytical solutions—from designing Snowflake based data models to building Power BI reports and dashboards that support reporting, insights, and ad hoc analysis across the organization. The role is responsible for developing and optimizing Snowflake tables, views, dynamic tables, and stored procedures using plain SQL, and for designing analytics friendly data models that are performant, trusted, and easy to consume. The position also plays a key role in ensuring data quality and reliability through automated testing, controlled deployments, and disciplined promotion practices across environments. In addition, this role supports the full analytics lifecycle by developing and maintaining Power BI datasets, reports, dashboards, subscriptions, and refresh workflows, while troubleshooting data or visualization issues that impact business users. This is a high impact individual contributor role within the Analytics team, with direct influence on data trust, analytical efficiency, and business decision making.

Requirements

Strong background in analytics engineering, data platforms, or data-driven roles supporting enterprise reporting, dashboards, and advanced analytical use cases.
Deep hands-on Python and SQL with the ability to design, develop, and optimize analytics-ready datasets, strong understanding of relational database concepts, dimensional modeling, and data relationships for scalable analytics consumption.
Experience working with BI and analytical tools (e.g., Power BI, Excel), with the ability to translate data into actionable insights for business users.
Proven ability to independently solve complex, end-to-end data and analytical challenges with a strong emphasis on data accuracy, consistency, and quality.
Strong communication and data storytelling skills, with the ability to clearly articulate data logic, trends, and insights to technical and senior business stakeholders.
Experience supporting cloud-based analytics platforms (e.g., Snowflake), with familiarity in governance, data quality frameworks, and controlled promotion practices in enterprise environments.
Bachelor’s degree in Computer Science or a related field; background in analytics, data science, or other quantitative disciplines preferred.

Nice To Haves

Experience in payments or financial services is a plus.

Responsibilities

Lead the design and implementation of enterprise-scale, analytics-ready data models in Snowflake, enabling scalable reporting, dashboards, and downstream data consumption.
Architect, build, and optimize highly scalable, resilient data pipelines across cloud environments (including Azure and Snowflake), ensuring robust data quality, security, and operational stability.
Own and optimize Jenkins CI/CD pipelines, resolving complex build, deployment, and runtime issues across environments.
Champion GitLab and Git best practices, driving high-quality code standards through disciplined version control, code reviews, and collaborative development.
Lead Snowflake deployment strategies using schema change (or equivalent tooling), ensuring consistent, auditable, and repeatable releases aligned with enterprise platform standards.
Own the end-to-end promotion lifecycle (dev → test → prod), establishing best practices for release management, governance, and environment stability.
Design and implement enterprise-grade data quality frameworks to ensure accuracy, completeness, and reliability of critical data assets.
Develop and maintain Python-based solutions for automation, orchestration, testing, and system integrations at scale.
Build or support JavaScript-based logic (e.g., Snowflake stored procedures) as required for platform extensibility.
Provide advanced Power BI support, including Snowflake-backed dataset optimization, report and workspace governance, subscription configuration (including dynamic), and troubleshooting performance and delivery issues.
Apply ML/AI concepts (feature engineering, model lifecycle, evaluation) to influence data modeling, data platform design, and future-state architecture.
Lead production support and operational excellence, including incident management, root cause analysis, and proactive issue mitigation for data and pipeline ecosystems.
Independently deliver complex, high-impact data solutions, demonstrating strong technical judgment, ownership, and execution in a fast-paced, evolving environment.
Partner cross-functionally with analytics, product, and business stakeholders to enable advanced analytics, refine data definitions, resolve data challenges, and ensure trusted, business-aligned datasets.
Act as a technical leader and subject matter expert, providing guidance through design reviews, code reviews, and knowledge sharing to elevate engineering standards and drive platform consistency.

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