Senior Vice President, Full Stack Data Engineer

BNY Mellon•New York, NY

1d•$116,500 - $220,000•Onsite

About The Position

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.

Requirements

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, including feature engineering pipelines, vector stores, RAG knowledge pipelines, or LLM context preparation workflows.
Experience operating in regulated environments (financial services, healthcare) with data governance, lineage, and compliance requirements.
Strong data modeling judgment: dimensional modeling, data vault, OBT patterns — knowing when to apply which and why.
Comfort operating in ambiguity and driving data discovery with senior stakeholders and data owners.
Experience with metadata management and governance platforms (Collibra, DataHub, OpenMetadata).
Familiarity with real-time and streaming data patterns (Kafka, Kinesis, Flink) as a complement to batch workloads.
Experience balancing pipeline velocity with data quality, observability, and SLA commitments.
Strong Java\Python engineering skills for pipeline development; SQL fluency (T-SQL, PL/SQL, or equivalent) for transformation and analysis.
Experience with dbt for transformation layer development and testing.
Proficiency with orchestration tooling: Airflow, Prefect, or equivalent.
Cloud data platform experience: Snowflake, Databricks, BigQuery, or Redshift in production.
Familiarity with cloud infrastructure relevant to data workloads: AWS (Glue, Lambda, Step Functions, S3, Redshift), Azure (Data Factory, Synapse, ADLS), or GCP (Dataflow, BigQuery, Cloud Composer).
Data quality and observability tooling: Great Expectations, Monte Carlo, dbt tests, or equivalent.
Version control, CI/CD, and DevOps practices applied to data pipeline development (DataOps).
Strong written and verbal communication across technical and non-technical audiences, including data owners, analytics consumers, and platform stakeholders.
Clear data product and delivery judgment within a scoped engagement.
Ability to coordinate and execute across stakeholders — data owners, platform engineers, analytics teams — without formal authority.
Practical tradeoff thinking: pipeline complexity vs. maintainability, freshness vs. cost, schema flexibility vs. governance.
Bias toward action with disciplined follow-through on data quality and operational readiness.

Responsibilities

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.