ITTConnect
ITTConnect

Data Platform Lead (Banking)

Miami, FL, US • Posted 4 days ago • Updated 5 hours ago
Full Time
On-site
Fitment

Dice Job Match Score™

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Job Details

Skills

  • Finance
  • PySpark
  • Management
  • Lifecycle Management
  • IT Security
  • Information Security
  • Data Warehouse
  • Workflow
  • Data Domain
  • Dimensional Modeling
  • Data Flow
  • Inventory
  • Documentation
  • Production Support
  • Accountability
  • Risk Management
  • Artificial Intelligence
  • Use Cases
  • Collaboration
  • Business Data
  • Regulatory Compliance
  • Data Engineering
  • Analytics
  • Data Architecture
  • Microsoft SQL Server
  • Orchestration
  • Data Governance
  • Meta-data Management
  • Access Control
  • Migration
  • Databricks
  • Unity
  • Data Quality
  • Continuous Integration
  • Continuous Delivery
  • Cloud Computing
  • Amazon Web Services
  • Amazon S3
  • Network Security
  • Auditing
  • Banking
  • Financial Services
  • Fluency
  • Portuguese
  • Spanish

Summary

Job Description
ITTConnect is seeking a Data Intelligence Platform Lead for a direct-hire full time position with a client that is a large financial institution.

Position is hybrid in Miami.

Our client is at a strategic moment in our data platform transformation, migrating from an on premise environment to a modern cloud native stack based on AWS, Databricks, and PySpark. They are seeking a hands-on, business-oriented data platform leader to lead a 10 people team in order to manage the bank's end-to-end data intelligence platform.

The successful candidate will coordinate the modernization of the current on-premises data environment while leading the design and implementation of a governed cloud data platform on Databricks and AWS. This is a strategic role for a professional who can connect architecture, governance, delivery execution, data quality, security, and AI adoption into a coherent enterprise data capability.

Key Responsibilities:

Cloud Data Platform and Migration

  • Coordinate the structuring of the Databricks environment on AWS, including development pipelines, operational controls, governance parameters, data quality monitoring, alerting, and platform observability.
  • Define and align target-state solutions for ingestion, orchestration, processing, monitoring, security, and lifecycle management in the Databricks ecosystem.
  • Lead the migration of on-premises data pipelines to Databricks, ensuring they are rebuilt as reusable, scalable, governed, and well-documented data products.
  • Partner with technology, security, infrastructure, compliance, and business stakeholders to ensure the cloud platform meets banking-grade operational, regulatory, and information security expectations.

Data Products, Governance, and Quality

  • Coordinate the definition, documentation, and dissemination of the data product concept across the Data team and the broader bank.
  • Establish the required governance, ownership, metadata, lineage, access, quality, monitoring, and lifecycle dimensions for data products.
  • Review and strengthen governance practices in the current data warehouse environment, including data access workflows, pipeline development standards, orchestration processes, and data domain definitions.
  • Define and document data quality dimensions, implement automated quality tests, and build end-to-end monitoring and alerting for critical data flows.

On-Premises Platform Modernization

  • Coordinate DataSecOps practices to establish end-to-end monitoring and alerting across infrastructure, development environments, orchestration layers, and data pipelines.
  • Lead the inventory, technical assessment, rationalization, and recommendation process for SQL Server environments, including whether to migrate, retain, consolidate, modernize, or decommission each server.
  • Drive improvements in operational reliability, documentation, development standards, and production support for the current SQL Server, Airflow, and dbt environment.

AI Enablement and Governance

  • Coordinate the establishment of AI governance practices, including principles, controls, accountability, observability, and risk management considerations.
  • Identify, prioritize, and coordinate AI initiatives that generate measurable business value on top of both the current on-premises environment and the future cloud data platform.
  • Support experimentation and delivery of AI-based use cases in collaboration with business, data, technology, compliance, and risk stakeholders.

Requirements
  • 15+ years of experience in IT.
  • Strong experience leading data platform, data engineering, analytics engineering, or data architecture initiatives in complex enterprise environments.
  • Practical understanding of Databricks, AWS data services, SQL Server, Airflow, dbt, data ingestion patterns, orchestration, monitoring, and platform operations.
  • Demonstrated ability to translate data governance, data quality, metadata, lineage, access control, and observability requirements into practical engineering standards.
  • Experience coordinating platform migrations or modernization initiatives from legacy or on-premises environments to cloud-based architectures.
  • Experience with Databricks Lakehouse architecture, Unity Catalog, data quality frameworks, CI/CD pipelines, and cloud-native monitoring practices.
  • Experience with AWS services commonly used in data platforms, such as S3, IAM, networking, security controls, monitoring, and infrastructure automation.
  • Familiarity with regulatory, security, and audit expectations in banking or financial services.
  • Highly desirable fluency in Portuguese and/or Spanish.
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.
  • Dice Id: 91021855
  • Position Id: cfca08da4c6648de54af02b96de85129
  • Posted 4 days ago
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