VP, Process Improvement

State Street

Data Engineer building governed pipelines and data products for State Street’s AI-driven asset-servicing operations. Enabling automation, agentic workflows, data quality, and auditability across Global Delivery.

Posted 8/11/2026full-timeBangalore • 🇮🇳 IndiaLeadWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in designing and operating scalable data pipelines, ensuring data quality and governance in AI-driven environments. Proficient in collaborating with cross-functional teams to align data strategies with business objectives and operational needs.

Highest-signal resume keywords

Data EngineeringData Quality ManagementSQL ProficiencyPython for Data ProcessingETL/ELT Patterns

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data Pipeline DevelopmentData Lifecycle ManagementMetadata ManagementData ValidationData IntegrationData WarehousingData LakesWorkflow OrchestrationAI and ML SupportData Cataloging

Soft Skills

CollaborationCommunicationProblem-SolvingOwnershipInfluence

Tools & Technologies

APIsCSVXMLData Quality ToolsEngineering Documentation

Industry Keywords

Financial ServicesOperational AnalyticsAI-Driven AutomationRegulated EnvironmentsComplex Operational Domains

Tech Stack

Tools & technologies

CloudETLPythonSQL

About the role

Key responsibilities & impact

Design, build, and operate scalable, resilient data pipelines for operational analytics, reporting, and AI-driven automation across cloud and on-prem environments
Model, store, and serve large-scale datasets for analytical workloads and low-latency AI and agent-based systems
Integrate vendor feeds, APIs, files, and enterprise platforms
Ensure pipelines are observable, reliable, production-ready, and operationally rigorous
Act as Data Steward for assigned business services, owning data quality, lineage, lifecycle management, definitions, critical data elements, dictionaries, glossaries, and metadata
Define and enforce data standards, controls, and documentation
Translate business control requirements into data-level and AI control mechanisms
Enable AI and intelligent automation with governed data for training, inference, and decisioning
Define agent constraints, data quality gates, and human-in-the-loop triggers
Ensure auditability and traceability through decision logs, lineage, and versioning of rules, prompts, and models
Establish data quality rules and exception taxonomies; monitor dashboards, triage issues, and coordinate remediation
Align data architecture and integrations with ecosystem dependencies, costs, and execution plans
Partner with Product Owners to align data definitions and metrics with business intent and outcomes
Collaborate with engineering, AI, platform, and business teams on simplification and automation use cases
Communicate technical concepts to non-technical stakeholders and drive adoption of AI-enabled solutions
Champion modern data and engineering practices across organizational boundaries

Requirements

What you’ll need

5+ years of hands-on experience in data engineering, preferably in platform, infrastructure, or large-scale enterprise environments
Strong engineering and systems mindset with production-grade data pipeline experience
Deep understanding of data lifecycle management, data quality, metadata, and controls in regulated environments
Experience with business stakeholders in complex operational domains such as fund accounting, middle office, custody, payments, or transfer agency
Strong SQL skills for data validation and analysis
Working knowledge of Python or similar for data processing, automation, or integration
Understanding of ETL/ELT patterns, APIs, file-based integrations, CSV, XML, and vendor feeds
Understanding of data warehouses, data lakes, and analytical data models
Understanding of workflow orchestration and scheduling tools
Experience with data cataloging, data quality tools, and engineering documentation
Experience supporting AI, ML, or generative AI systems through data engineering and governance
Familiarity with agent-based systems, human-in-the-loop workflows, model/prompt grounding, and decision traceability
Ability to assess data risks, controls, and guardrails in AI-driven operational workflows
Degree in Computer Science, Engineering, or equivalent practical experience in the financial services domain
Hands-on ownership of outcomes end-to-end
Ability to operate in a small, high-impact team with significant organizational visibility and influence

Benefits

Comp & perks

Inclusive development opportunities
Flexible work-life support
Paid volunteer days
Vibrant employee networks