This listing doesn't post a salary. Most similar roles pay
$191,250–$264,500.
Based on 240 similar postings.
Employer
About Early Warning Services
Early Warning Services is a fintech company that operates the Zelle person-to-person payments network, the Paze digital checkout wallet, and Certos fraud prevention and identity risk solutions for financial institutions.
Early Warning Services currently has
74 open roles
on FindRole.
Listed pay typically runs
$145,000–$175,000
across 39 roles with salary data.
Sr Director, Data Platform & Analytics Engineering leads the engineering capabilities powering the enterprise data platform, governed data products, and product analytics. This leader manages the modernization of legacy systems, ensuring reliable data consumption across business and product use cases while establishing scalable patterns for ingestion, transformation, and domain modeling. The role involves building trusted data products to improve availability and quality for critical reporting. Key technical requirements include experience with Databricks, Snowflake, Spark/PySpark, Kafka, and AWS, alongside proficiency in batch, streaming, API, and file-based integration. Core competencies include data modeling, lineage, observability, and performance management. The role operates within the financial services and payments domain, specifically addressing challenges related to fraud, identity, and highly regulated environments while ensuring the security and integrity of sensitive information during large-scale infrastructure transitions.
Execute the roadmap for modernizing legacy data platforms and migrating workloads to the Enterprise Data Platform.
Establish scalable engineering patterns for ingestion, transformation, domain modeling, and governed data products.
Translate business outcomes and requirements into prioritized engineering roadmaps and reusable data capabilities.
Develop trusted enterprise data products to support product, customer, operational, and corporate analytics.
Maintain the reliability of existing critical data and reporting services during platform transitions.
Establish engineering standards for data quality, testing, lineage, observability, and cost management.
Lead, mentor, and manage engineering leaders and teams using a product-based operating model.
Manage engineering capacity, vendor partnerships, and investments against business transformation priorities.
What we're looking for
Candidates must possess the eligibility to work in the United States at the date of hire.
15+ years of experience across Data Engineering, Data Platforms, Analytics Engineering, Data Architecture, Business Intelligence, or related disciplines.
10+ years of experience designing and delivering large-scale enterprise data and analytics solutions.
5+ years of experience building and leading high-performing data/platform engineering organizations and leaders.
Demonstrated experience defining and executing enterprise-scale data-platform or analytics modernization strategies and roadmaps.
Experience delivering large-scale data solutions across cloud, on-premises, and hybrid environments.
Strong understanding of modern data engineering, data modeling, governed data products, and analytics engineering architectures.
Experience managing significant engineering programs, capacity, budgets, and external partners.
Experience modernizing legacy systems to cloud platforms using Databricks, Snowflake, Spark/PySpark, Kafka, or AWS (preferred).
Experience with batch, streaming, API, and file-based data integration patterns (preferred).
Experience in financial services, payments, fraud, identity, or other highly regulated environments (preferred).
Data Engineering
Data Infrastructure
Database Engineering
Data Governance
Analytics Engineering
Data Quality
Data Architecture
Machine Learning Platform
Data Pipelines
GCP
Databricks
DataOps
MLOps
Infrastructure as Code
CI/CD
Data Engineering
Distributed Systems
Identity and Access Management
Data Governance
Cloud Security
System Design
Data Platforms
AI
Conversational AI
Text Summarization
Performance Optimization
Scalability
Observability
Code Review
Architecture
Platform Modernization