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Senior Data Engineering Manager, Financial Platform at Block
BlockSeattle, WA, United States of America·1w ago
SeniorFull-timePythonRustAWSGCPData ScienceAI
Salary Range
$132,600 - $179,400 /year
This salary is estimated based on similar roles. The actual salary may vary.
About the Role
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.
Key Responsibilities
- →Lead and grow the FinPlat data engineering team that builds and maintains the financial data warehouse and lakehouse foundations
- →Own the strategy and architecture for financial data pipelines and models that support automation, monitoring, and intelligence
- →Partner closely with FinPlat Data Science, financial operations teams, and other stakeholders
- →Represent data engineering for financial domains, communicating clearly with technical and nontechnical partners
- →Collaborate with other DE leaders to advance a standardized, functional, and AI ready data platform
- →Raise the quality, trust, and performance of core financial data systems
Requirements
- ✓12+ years of experience in data engineering or related software engineering field
- ✓Strong software engineering fundamentals with experience designing and scaling distributed data systems
- ✓Deep understanding of financial data domains such as acquiring, issuing, chargebacks, disputes, and banking adjacent data
- ✓Experience building and operating large scale data warehouse and lakehouse systems on Snowflake, Databricks, or similar platforms
- ✓Proficiency in SQL and Python combined with broader engineering skills
- ✓Experience with orchestration and compute frameworks like Airflow, Prefect, Spark, and cloud native services across AWS or GCP
- ✓Knowledge of data modeling at scale, quality and validation frameworks, data contracts, semantic layer concepts
- ✓Ability to partner effectively with finance, product, data science, and operations teams
- ✓Experience coaching engineering leaders, shaping team culture
- ✓Ability to guide adoption of AI first patterns and standards
- ✓Strong communication skills with the ability to represent data engineering in both technical and nontechnical discussions
