Summary

The Financial Platform ("FinPlat") Data Engineering lead manages the team responsible for the financial data backbone that powers reporting, monitoring, and analytics across Block payment and money movement flows. This role oversees the data warehouses and lakehouse systems that support core financial domains such as acquiring, issuing, chargebacks, disputes, and banking data. You will ensure the team delivers trusted data products that enable accurate operational decision making for finance, product, and other partner teams across Block.

You Will

1. Lead and grow the FinPlat data engineering team that builds and maintains the financial data warehouse and lakehouse foundations that power operational, analytical, and AI driven decision support systems
2. Own the strategy and architecture for financial data pipelines and models that support automation, monitoring, and intelligence across Block payment flows
3. Partner closely with FinPlat Data Science, financial operations teams, banking product managers, and other stakeholders to shape data needs and deliver trusted domain products
4. Represent data engineering for financial domains, communicating clearly with technical and nontechnical partners to guide decisions and align on long term direction
5. Collaborate with other DE leaders to advance a standardized, functional, and AI ready data platform across Block
6. Raise the quality, trust, and performance of core financial data systems to support accurate reporting, monitoring, and operational workflows

You Have

1. 12+ years of experience in data engineering or a related software engineering field, including 4+ years leading or managing engineering teams
2. Strong software engineering fundamentals, with experience designing and scaling distributed data systems that handle high volume, high integrity financial data
3. Deep understanding of financial data domains such as acquiring, issuing, chargebacks, disputes, and banking adjacent data
4. Experience building and operating large scale data warehouse and lakehouse systems on Snowflake, Databricks, or similar platforms, including performance tuning and cost management
5. Proficiency in SQL and Python combined with broader engineering skills such as version controlled development workflows, code review, automated testing, and modern CI practices
6. Experience with orchestration and compute frameworks like Airflow, Prefect, Spark, and cloud native services across AWS or GCP
7. Knowledge of data modeling at scale, quality and validation frameworks, data contracts, semantic layer concepts, and patterns for AI and automation readiness
8. Ability to partner effectively with finance, product, data science, and operations teams to drive alignment, clarity, and shared outcomes
9. Experience coaching engineering leaders, shaping team culture, and raising execution and architectural quality across teams
10. Ability to guide adoption of AI first patterns and standards that improve speed, reliability, learning, and quality across data systems
11. Strong communication skills with the ability to represent data engineering in both technical and nontechnical discussions

Zone A:

$239,600—$359,400 USD

Zone B:

$239,600—$359,400 USD

Zone C:

$239,600—$359,400 USD

Zone D:

$239,600—$359,400 USD

Skills

• Communications Skills
• Development
• Software Architecture
• Software Engineering
• Team Collaboration