Principal Data Engineer

This position follows a hybrid work model, with an expectation to be in the office 3 days per week at the St. Petersburg, FL Corporate Office location.

Please note: This role is not eligible for Work Visa sponsorship, either currently or in the future.

Responsibilities

Technical Strategy & Architecture

• Master Data Platforms and other enterprise-scale data platforms, ensuring scalability, reliability, availability, performance, and enterprise-wide reuse.

• Establish engineering standards, data architecture patterns, integration patterns

• Define and drive the target-state architecture for enterprise Security/Product , and design principles for mission-critical master data and enterprise-scale data platforms.

• Lead system design and modernization roadmaps for high-impact initiatives across security, product, master data, and other enterprise-scale data domains.

Engineering Leadership

• Provide technical leadership across multiple teams, not limited to a single project or squad.

• Act as a trusted advisor to leadership on technology strategy, trade-offs, and long-term platform evolution.

• Drive alignment across engineering, data, and platform teams to ensure consistency and reusability.

Solution Design & Development

• Lead the design and development of enterprise-grade Python applications and distributed systems.

• Oversee architecture and implementation of data pipelines, APIs, and large-scale data processing frameworks.

• Ensure solutions are designed with high availability, fault tolerance, and observability.

Data & Database Engineering

• Lead end-to-end engineering ownership for mission-critical database and master data platforms, including development, support, maintenance, lifecycle management, performance, reliability, and operational excellence.

• Apply advanced database optimization strategies across Oracle and related platforms, including performance tuning, partitioning, query optimization, resiliency, recoverability, and scalability.

• Ensure efficient data modeling, storage, and access patterns across platforms.

• Apply hands-on expertise in Oracle/ODI, Python, Spark, AWS EMR, Redshift, S3, and Iceberg to design, build, and modernize enterprise data platforms.

• Lead transition of legacy and operational master data capabilities toward modern cloud data lakehouse architecture using S3, Iceberg, Redshift, and medallion patterns for ingestion, transformation, curation, quality, and governed consumption.

Data Understanding

• Strong functional and data understanding of Security/Product Master Data Platforms is required, including how enterprise master data is modeled, governed, integrated, consumed, and supported.

• Must have experience across core enterprise data domains, including Clients, Accounts, Assets and Liabilities, Trades and Activities, Security/Product Master, with particular depth in Security/Product Master Data.

• Must have hands-on experience working with master data platforms supporting these domains, including business meaning, reference data, data lineage, data quality, integration, stewardship, downstream consumption, and operational support patterns.

• Ability to partner with business, architecture, governance, operations, and application teams to translate functional data needs into durable platform capabilities and reusable data products.

CI/CD & Platform Engineering

• Architect and standardize CI/CD pipelines using Jenkins and modern DevOps practices.

• Drive adoption of automation-first principles across build, test, and deployment workflows.

• Promote DevSecOps best practices and governance controls.

Cloud & Containerization

• Lead cloud modernization of enterprise master data capabilities, including transition to cloud data lakehouse architecture and medallion-based bronze, silver, and gold data layers.

• Influence AWS-based data architecture decisions, including appropriate use of Spark, AWS EMR, cloud storage, orchestration, data quality controls, and scalable consumption patterns.

• Drive modernization while preserving operational continuity, enterprise availability, data trust, cost discipline, security, and compliance expectations.

Performance, Reliability & Scalability

• Establish frameworks for performance engineering, observability, monitoring, SLAs, SLOs, SLIs, and operational readiness for enterprise-wide master data platforms.

• Lead root cause analysis of critical production issues and define systemic improvements across platform reliability, data quality, resiliency, recovery, and downstream dependency management.

• Ensure the platform meets enterprise-grade resiliency, recovery, security, and availability requirements for mission-critical data distribution.

AI-Enabled Engineering

• Demonstrate proficiency in using AI tools and AI-assisted engineering practices to improve individual and team efficiency, productivity, solution quality, and delivery velocity.

• Use AI responsibly to accelerate analysis, design, coding, testing, documentation, troubleshooting, and operational support while maintaining appropriate engineering judgment, security, and governance standards.

• Identify opportunities to apply AI to develop better technical solutions, reduce manual effort, improve platform reliability, and increase the speed of modernization initiatives.

Mentorship & Talent Development

• Mentor senior engineers and leads, raising the overall technical bar of the organization.

• Drive knowledge sharing, standards adoption, and engineering excellence initiatives.

• Serve as a role model for engineering best practices and problem-solving.

Technical Skills

• Expert-level proficiency in data engineering and large-scale data processing using Python, Oracle/ODI, Spark, AWS EMR, Redshift, Iceberg, distributed systems design, and modern platform engineering patterns.

• Deep expertise in CI/CD using Jenkins, Kubernetes, and containerization.

• Strong experience designing, building, and operating data pipelines, APIs, batch and near-real-time data processing capabilities, data quality controls, lineage, and enterprise consumption patterns.

• Experience modernizing legacy or operational platforms into cloud data lakehouse architecture using medallion patterns, including bronze, silver, and gold layers, curated data products, and governed consumption.

• Experience developing, supporting, maintaining, and modernizing mission-critical database and master data platforms in production environments.

• Strong functional understanding of enterprise master data domains, especially Security/Product Master, with exposure to Clients, Accounts, Assets and Liabilities, Trades, and Activities.

• Proficiency using AI tools and AI-assisted engineering practices to improve productivity, accelerate delivery, enhance solution design, improve documentation, and increase engineering velocity.

• Advanced knowledge of database performance optimization, data modeling, platform reliability, and production support for mission-critical data platforms.

• Strong understanding of cloud platforms, including AWS, Azure, and GCP, and modern architecture patterns.

• Experience in enterprise architecture, system integration, and platform engineering.

Leadership & Behavioral Competencies

• Strategic thinker with ability to connect technical decisions to business outcomes

• Strong influence skills across teams without direct authority

• Exceptional problem-solving and system-level thinking

• Excellent communication skills, including ability to engage with senior leadership and stakeholders

Required Qualifications :

• 15+ years of progressive engineering experience, including technical leadership for enterprise-scale data, database, or platform engineering capabilities.

• Strong hands-on data engineering experience with Python, Oracle/ODI, Spark, AWS EMR, Redshift, Iceberg, and large-scale data processing patterns.

• Experience developing, supporting, maintaining, and modernizing mission-critical database, master data, or enterprise-scale data platforms in production environments.

• Strong functional and data understanding of Security/Product Master Data Platforms, with working knowledge of enterprise data domains such as Clients, Accounts, Assets and Liabilities, Trades, and Activities.

• Experience with cloud data lakehouse architecture, medallion patterns, data quality controls, lineage, governed consumption, and reusable data product patterns.

• Proficiency using AI tools and AI-assisted engineering practices to improve productivity, solution quality, documentation, troubleshooting, and delivery velocity.

• Strong communication, technical influence, and cross-functional leadership skills with the ability to engage engineering, architecture, governance, operations, product, and business stakeholders.

• Experience in Agile/Scrum at scale, such as SAFe or similar frameworks.

• Experience in financial services, wealth management, capital markets, Security/Product Master Data, or enterprise reference data platforms.

• Experience leading modernization of enterprise master data or reference data platforms from legacy technologies toward cloud data lakehouse, medallion architecture, or governed data product models.

• Experience with enterprise data governance, data quality, lineage, stewardship, metadata management, and enterprise consumption patterns at scale.

• Experience applying AI-enabled engineering practices to improve development efficiency, operational productivity, solution quality, and delivery velocity.