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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Director, Fellow, Data Architect - **Company:** The Bank of New York Mellon Corporation - **Location:** New York, NY, United States - **Experience:** Expert - **Salary:** $285,000.0 - **Contract:** Permanent contract - **Skills:** Airflow, Apache HTTP Server, Automation of Tests, Microsoft Azure, Cloud Engineering, Software Quality, Information Systems, Continuous Integration, Data Architecture, Information Engineering, Data Infrastructure, Data Systems, DevOps, Distributed Computing Environment, Oracle Exadata, Identity and Access Management, Python (Programming Language), Metadata, Software Construction, Software Engineering, Software Systems, SQL Databases, Data Streaming, Web Application Frameworks, Enterprise Data Management, Parquet, Snowflake, Technical Debt, Containerization, Data Lakes, Kubernetes, Infrastructure Automation Frameworks, Information Technology, Apache Kafka, Data Management, Software Version Control - **Published:** July 15, 2026 - **Apply:** https://diversityjobs.com/main/sendform/8/8/28176/1/17589333?backUrl=%2Fcareer%2F17589333%2FSenior-Director-Fellow-Data-Architect-New-York-New-York ## About the Role Required * Bachelor's degree in Computer Science, Engineering, Information Systems, or a related discipline, or equivalent practical experience. * Significant experience in data engineering, software engineering, or data platform engineering. * Proven ability to deliver enterprise-scale data and software solutions in complex environments. * Strong hands-on experience with modern data technologies * Strong proficiency in Python, SQL, and at least one additional language such as Java or Scala. * Experience with distributed data processing and pipeline design using modern frameworks and tooling. * Experience with DevOps and software delivery practices including CI/CD, version control, automated testing, and infrastructure automation. * Strong understanding of data architecture, data modeling, large-scale systems design, and platform engineering concepts. * Demonstrated ability to influence technical decisions across teams and functions. * Strong communication and stakeholder management skills. Preferred * Experience building shared or multi-tenant enterprise data platforms. * Familiarity with open data ecosystem technologies such as Parquet, Delta Lake, or similar table and storage formats. * Experience with orchestration and workflow technologies such as Airflow or equivalent tools. * Experience with streaming and event-driven technologies such as Kafka or similar platforms. * Familiarity with containerization and cloud-native deployment approaches such as Kubernetes. * Experience with metadata, lineage, data quality, and governance tooling. * Experience operating in highly regulated, large-scale enterprise environments. * Exposure to modern data operating models, including data products, domain-oriented ownership, or data mesh-aligned patterns., * Data engineering and software delivery * Enterprise platform architecture * Strategic thinking and roadmap development * Stakeholder influence and cross-functional leadership * Technical problem solving and decision-making * Engineering quality and operational excellence * Communication and executive presence * Continuous improvement and innovation mindset ## Description The successful candidate will bring deep experience delivering enterprise-grade data solutions using modern technologies while also helping shape the target-state architecture for a scalable, resilient, and reusable data platform. This individual must be equally comfortable building software, driving technical direction, partnering across functions, and influencing platform decisions that extend beyond their immediate team. The position is location in New York, NY., Engineering Delivery * Design, build, test, deploy, and support high-quality data products and platform capabilities for enterprise use cases. * Deliver scalable and reliable software solutions across batch, near-real-time, and event-driven data patterns, as needed. * Apply strong software engineering discipline, including code quality, automated testing, CI/CD, observability, and documentation. * Drive engineering excellence across the delivery lifecycle, with a focus on stability, maintainability, performance, and reuse. * Ensure data solutions are production-ready and aligned to enterprise standards for security, resiliency, and operational support. Data Product and Platform Development * Build modern data products that are consumable, well-governed, and aligned to business and platform objectives. * Contribute to the design and evolution of a large-scale, multi-business data platform supporting diverse data domains and consumption models. * Define and implement scalable patterns for ingestion, transformation, storage, metadata, lineage, access management, and delivery. * Develop reusable services, frameworks, and engineering patterns that accelerate delivery across teams. * Support cloud and hybrid data architectures using modern storage and compute approaches. Architecture and Technical Leadership * Architect enterprise-scale data capabilities leveraging Snowflake, Apache Iceberg, Oracle Exadata, Azure, and other relevant technologies. * Evaluate current-state architecture, identify opportunities for simplification and modernization, and recommend target-state solutions. * Make sound design decisions that balance business outcomes, risk management, scalability, cost, and speed of execution. * Contribute to enterprise engineering standards, architecture patterns, and platform guardrails. * Lead through technical depth, strong judgment, and the ability to convert strategy into practical engineering outcomes. Strategic Roadmap and Organizational Influence * Translate business priorities and platform strategy into actionable technical roadmaps and delivery plans. * Partner with product, architecture, infrastructure, security, governance, and business stakeholders to align on priorities and sequencing. * Influence decisions across the broader organization through strong communication, partnership, and credibility. * Identify and address technical debt, platform gaps, and delivery risks in a proactive and structured way. * Contribute to longer-term platform strategy while ensuring strong near-term execution against commitments. Collaboration and Leadership * Work effectively across a matrixed organization and multiple lines of business. * Build strong relationships with engineering teams, platform teams, architects, and business partners. * Communicate complex technical topics clearly to both technical and non-technical stakeholders. * Mentor team members and help elevate engineering and delivery practices across the organization. * Demonstrate ownership, accountability, curiosity, and a strong bias for execution., * Help define and implement the architecture for a large-scale multi-business data platform. * Drive strategic roadmap execution while balancing immediate delivery needs. * Influence engineering and platform direction across organizational boundaries. * Bring a product-oriented mindset to data, with strong focus on usability, quality, and business value. * Operate with a high degree of ownership, collaboration, and accountability. ## Related Videos - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Parquet, Delta, Iceberg & Ducklake - An introduction for developers](https://www.wearedevelopers.com/videos/100075-parquet-delta-iceberg-ducklake-an-introduction-for-developers) - [From DevOps to Scaled DevOps: How We’re Rebuilding Continuous Delivery as a Platform](https://www.wearedevelopers.com/videos/100018-from-devops-to-scaled-devops-how-we-re-rebuilding-continuous-delivery-as-a-platform) - [Enjoying SQL data pipelines with dbt](https://www.wearedevelopers.com/videos/823-enjoying-sql-data-pipelines-with-dbt) - [AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment](https://www.wearedevelopers.com/videos/1152-ai-model-management-life-circles-ml-ops-for-generative-ai-models-from-research-to-deployment) - [OLAP for AI Applications and why you should care](https://www.wearedevelopers.com/videos/100212-olap-for-ai-applications-and-why-you-should-care) ## Related Articles - [Top Big Data Technologies That You Need to Know](https://www.wearedevelopers.com/magazine/108-top-big-data-technologies-that-you-need-to-know) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [Data Engineer Salary UK](https://www.wearedevelopers.com/magazine/253-data-engineer-salary-uk) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story) - [Data Analyst Salary in the UK](https://www.wearedevelopers.com/magazine/278-data-analyst-salary-in-the-uk)