Principal Data Engineer

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., * 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.

• 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., * 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, * 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.