Senior Data Engineer – AI & Intelligent Automation, GD Simplification Vice president

Role Overview

We are hiring a senior, hands-on Data Engineer to play a pivotal role in transforming Global Delivery operations through data-driven automation, generative AI, and agent-based systems. This is not a traditional data engineering role.

This role sits at the intersection of data engineering, AI enablement, and business simplification, owning the end-to-end data foundation that powers intelligent workflows used directly in core operational processes. You will act as the data owner and steward for high-impact simplification initiatives, ensuring data quality, control, auditability, and fitness-for-purpose across analytics, automation, and AI decisioning.

You will work closely with Product Owners, AI engineers, and platform teams to design and operate production-grade data pipelines and data products that enable scalable, secure, and observable AI-driven workflows across the asset servicing lifecycle.

Key Responsibilities

• Design, build, and operate scalable, resilient data pipelines supporting operational analytics, reporting, and AI-driven automation across cloud and on‑prem environments.

• Model, store, and serve large-scale datasets optimized for both analytical workloads and low-latency consumption by AI and agent-based systems.

• Integrate data from multiple internal and external sources, including vendor feeds, APIs, files, and enterprise platforms.

• Ensure pipelines are observable, reliable, and production-ready with clear ownership and operational rigor.

• Act as Data Steward for assigned business services within GD Simplification, accountable for:

  • Data quality, consistency, lineage, and lifecycle management

  • Business definitions, critical data elements (CDEs), and calculation logic

  • Data dictionaries, business glossaries, and metadata

• Define and enforce data standards, controls, and documentation aligned with governance and platform requirements.

• Translate business control requirements into data-level and AI control mechanisms.

AI & Agentic Systems Enablement

• Enable AI and intelligent automation by ensuring high-quality, well-governed inputs for training, inference, and decisioning.

• Define agent action constraints, data quality gates, and human‑in‑the‑loop triggers before automated actions are executed.

• Ensure auditability and traceability through agent decision logs, data lineage, and versioning of rules, prompts, and models.

Data Quality, Controls & Operations

• Establish data quality rules and exception taxonomies.

• Monitor data quality dashboards, triage issues, and coordinate remediation across upstream and downstream teams.

• Ensure data quality and control checks are embedded before AI-driven actions occur.

• Align data architecture and integrations with broader ecosystem dependencies, cost considerations, and execution plans.

Collaboration & Influence

• Partner closely with Product Owners to ensure data definitions and metrics align with business intent and measurable outcomes.

• Collaborate across engineering, AI, platform, and business teams to identify and prioritize high-value simplification and automation use cases.

• Communicate complex technical concepts clearly to non-technical stakeholders and help drive adoption of AI-enabled solutions across GD.

• Champion modern data and engineering practices across organizational boundaries.

Required Skills & Experience

• 5–10+ years of hands-on experience in data engineering, preferably in platform, infrastructure, or large-scale enterprise environments.

• Strong engineering and systems mindset with experience building production-grade data pipelines.

• Deep understanding of data lifecycle management, data quality, metadata, and controls in regulated environments.

• Experience working closely with business stakeholders in complex operational domains (e.g., fund accounting, middle office, custody, payments, transfer agency).

• Strong SQL skills for data validation and analysis.

• Working knowledge of Python (or similar) for data processing, automation, or integration.

• Solid understanding of: ETL / ELT patterns, APIs and file-based integrations (CSV, XML, vendor feeds), Data warehouses, data lakes, and analytical data models, Workflow orchestration and scheduling tools

• Experience with data cataloging, data