VP, Process Improvement

State Street

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

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

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and managing scalable data pipelines, ensuring data quality and governance in complex operational environments. Proficient in integrating diverse data sources and collaborating with cross-functional teams to drive AI-enabled solutions.

Highest-signal resume keywords

Data Engineering ExperienceSQL ProficiencyPython for Data ProcessingData Quality ManagementETL/ELT Understanding

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data Pipeline DevelopmentData Lifecycle ManagementData Quality AssuranceMetadata ManagementData IntegrationAnalytical Data ModelingWorkflow OrchestrationData CatalogingAI GovernanceData Validation

Soft Skills

CollaborationCommunicationProblem-SolvingOwnershipAdaptability

Tools & Technologies

APIsData Quality ToolsData WarehousesData LakesWorkflow Scheduling Tools

Certifications & Qualifications

Degree in Computer ScienceDegree in Engineering

Industry Keywords

Financial ServicesOperational AnalyticsAI-Driven AutomationRegulated EnvironmentsFund Accounting

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-premises environments
Model, store, and serve large-scale datasets for analytical workloads and low-latency AI and agent-based systems
Integrate data from 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, business definitions, critical data elements, calculation logic, 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 inputs for training, inference, and decisioning
Define agent action constraints, data quality gates, and human-in-the-loop triggers
Ensure auditability and traceability through decision logs, data lineage, and versioning of rules, prompts, and models
Establish data quality rules and exception taxonomies
Monitor data quality 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 outcomes
Collaborate with engineering, AI, platform, and business teams to prioritize 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 data engineering experience, preferably in platform, infrastructure, or large-scale enterprise environments
Experience building production-grade data pipelines
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
Experience with APIs and file-based integrations, including 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 practices
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
Passion for hands-on ownership and end-to-end outcomes
Comfortable operating 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