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, lifecycle management, business definitions, critical data elements (CDEs), 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 quality tools, and engineering documentation practices.
Experience supporting AI, ML, or generative AI systems through data engineering and governance.
Familiarity with concepts such as agent‑based systems, human‑in‑the‑loop workflows, model/prompt grounding, and decision traceability.
Ability to think critically about data risks, controls, and guardrails in AI‑driven operational workflows.
Education & Mindset
Degree in Computer Science, Engineering, or equivalent practical experience in the financial services domain.
Passion for being hands‑on, owning outcomes end‑to‑end, and building systems that materially change how work gets done.
Comfortable operating in a small, high‑impact team with significant visibility and influence across the organization.
Equal Opportunity Employer
As an Equal Opportunity Employer, we consider all qualified applicants for all positions without regard to race, creed, color, religion, national origin, ancestry, ethnicity, age, disability, genetic information, sex, sexual orientation, gender identity or expression, citizenship, marital status, domestic partnership or civil union status, familial status, military and veteran status, and other characteristics protected by applicable law.
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