Senior Vice President, Full Stack Data Engineer

Senior Vice President , Full-Stack Data Engineer At BNY, our culture allows us to run our company better and enables employees’ growth and success. As a leading global financial services company at the heart of the global financial system, we influence nearly 20% of the world’s investible assets. Every day, our teams harness cutting-edge AI and breakthrough technologies to collaborate with clients, driving transformative solutions that redefine industries and uplift communities worldwide. Recognized as a top destination for innovators and champions of inclusion, BNY is where bold ideas meet advanced technology and exceptional talent. Together, we power the future of finance – and this is what #LifeAtBNY is all about. Join us and be part of something extraordinary. We’re seeking a future team member for the role of Senior Vice President , Full-Stack Data Engineer to join our Engineering Hub Analytics team. This role is located in New York, NY This is a hands-on senior individual contributor role embedded within the Engineering Hub Analytics practice. The Data Engineer owns data platform delivery across client engagements — designing, building, and hardening production-grade data pipelines, warehouse architectures, and data infrastructure that power AI and analytics capabilities. This is not a consulting or coordination role; it is an engineering role with full delivery ownership.

 

In this role, you'll make an impact in the following ways:

• Design, build, and harden production data pipelines, ELT/ETL workflows, and data platform components across client engagements — moving confidently from prototype to scalable, observable production deployment.
• Embed with business and platform stakeholders to scope and execute time-boxed data engineering engagements with clear entry and exit criteria; translate defined data opportunities into production-ready delivery plans.
• Architect and implement data infrastructure across ingestion, transformation, serving, and governance layers using modern tooling (dbt, Airflow/Prefect, Spark, Snowflake, Databricks, cloud-native services).
• Build and integrate data pipelines that feed AI and analytics systems — including feature stores, RAG knowledge bases, semantic search indexes, and LLM context pipelines.
• Default to reuse-first delivery: extend existing data platform patterns, templates, and pipeline modules rather than building avoidable one-offs; contribute reusable data assets back to shared repositories.
• Apply data quality, observability, and operational readiness practices consistently — including lineage tracking, schema validation, SLA monitoring, and alerting.
• Execute discovery with data owners, analytics teams, and sponsors to clarify data contracts, validate feasibility, and rapidly prototype before hardening into production.
• Prepare clear handoff packages and transition plans — including data dictionaries, lineage documentation, pipeline runbooks, and ownership transfer artifacts — so receiving teams can sustain solutions independently.
• Surface reusable data patterns and learnings from engagements that can be standardized and promoted into shared platform capabilities.
• Coordinate with architecture, security, compliance, and governance stakeholders to ensure data solutions are production-appropriate, lineage-traceable, and governance-compliant.
• Mentor junior data engineers; contribute to team delivery quality, standards, and knowledge sharing.

 

To be successful in this role, we're seeking the following:

• Bachelor's degree in computer science or a related discipline, or equivalent work experience required; advanced degree is beneficial
• 15+ years of diverse experience in multiple areas of information technology required; experience in the securities or financial services industry is a plus. 
• Mentors junior data engineers within engagements; contributes to team delivery quality, pipeline standards, and knowledge sharing.
• Deep experience designing and operating production ELT/ETL pipelines, data warehouse/lakehouse architectures, and cloud data infrastructure.
• Hands-on experience with modern data tooling: dbt, Airflow or Prefect, Spark, Snowflake or Databricks or BigQuery, and cloud-native data services (AWS, Azure, or GCP).
• Experience working across the full data stack — ingestion, transformation, serving, governance, and quality — rather than only within a single layer.
• Experience delivering data infrastructure that feeds AI/ML systems, inclu