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Business Analyst, Vice President
senior · Data Science & Analytics
Senior Business Analyst – Next Gen Data Integration Project
Summary
The Senior Business Analyst will play a key role in the modernization of the firm's enterprise data ecosystem, supporting cloud-native data integration, data warehousing, and analytics initiatives. The role will partner with business and technology teams to define requirements and deliver scalable, secure, and configurable cloud-based data solutions leveraging platforms such as Snowflake, Azure, SQL Server, Oracle, and modern ETL/ELT technologies.
The Senior Business Analyst will focus on enterprise data integration workflows across source systems, ETL/ELT processes, data warehouse platforms, and financial data domains. The role will help define requirements for ingestion, transformation, validation, mapping, reconciliation, lineage, monitoring, exception handling, and data distribution capabilities across platforms such as Snowflake, Microsoft SQL Server, Oracle, and related ETL/data integration tools.
They must be comfortable working at the intersection of business, data, and technology, with the ability to translate complex financial data needs into clear business requirements, functional specifications, user stories, source-to-target mappings, acceptance criteria, and delivery plans. The ideal candidate will have strong Agile delivery experience, excellent communication skills, and the ability to bridge business stakeholders and technical teams to deliver scalable, configurable, and supportable enterprise data integration solutions.
Responsibilities
Research, analyze, and document business and technical requirements for enterprise data integration, data warehousing, and ETL modernization initiatives.
Work with business stakeholders, product managers, data architects, engineering leads, QA, operations, and implementation teams to gather requirements and define feature specifications.
Analyze financial data workflows across front, middle, and back-office processes, including inbound data feeds, outbound data distribution, operational data flows, reporting dependencies, and downstream warehouse consumption.
Produce clear business requirement documents, functional specifications, data mapping documents, source-to-target mapping specifications, user stories, acceptance criteria, and workflow documentation.
Partner with data architects and engineering teams to define ingestion, transformation, validation, enrichment, reconciliation, exception handling, and data promotion requirements.
Document data lineage, data quality rules, transformation logic, reference data dependencies, and business rules required to support trusted financial data across enterprise platforms.
Collaborate with technical teams to understand ETL/ELT workflows, SQL-based processing, batch and event-driven data flows, data warehouse design patterns, and integration touchpoints.
Support requirements and solution definition for Snowflake, Microsoft SQL Server, Oracle, and other database/data platform environments.
Work with ETL and data integration tools and teams to support modernization of legacy integration processes into scalable, configurable, and metadata-driven workflows.
Strong experience supporting cloud-based data platforms and enterprise data modernization initiatives.
Hands-on experience working with cloud data warehousing and analytics platforms such as Snowflake, Databricks, Microsoft Fabric, Azure Synapse, AWS Redshift, or equivalent technologies.
Experience gathering and documenting requirements for cloud-native ETL/ELT solutions using tools such as Azure Data Factory, Talend Cloud, Informatica Cloud, AWS Glue, or similar technologies.
Strong understanding of cloud architecture concepts, including data lakes, data warehouses, object storage, event-driven processing, APIs, microservices, and scalable distributed data processing.
Experience supporting migration of on-premise data platforms to cloud-based architectures.
Knowledge of cloud security, data privacy, access management, encryption, and regulatory compliance requirements for financial data.
Familiarity with DevOps, CI/CD pipelines, Infrastructure as Code (IaC), and automated deployment processes in cloud environments.
Understanding of modern data architecture patterns including Lakehouse, Data Mesh, Data Fabric, and metadata-driven integration frameworks.
Experience working in Azure, AWS, or Google Cloud Platform environments, with Azure and Snowflake experience strongly preferred.
Facilitate requirement workshops, design discussions, backlog refinement sessions, sprint planning, sprint reviews, and stakeholder review sessions.
Create and manage Jira stories, epics, acceptance criteria, priorities, dependencies, and backlog items in partnership with product and engineering leads.
Actively participate in Agile Scrum ceremonies, including backlog grooming, sprint planning, daily standups, sprint demos, retrospectives, and release planning.
Partner with QA to define test scenarios, data validation approaches, reconciliation checks, expected results, and end-to-end test coverage for data integration workflows.
Review and approve QA test plans, test results, data validation outcomes, and defect triage findings where appropriate.
Support UAT planning and execution, including business validation of data mapping, transformation logic, reporting outputs, and downstream data consumption.
Assist implementation, support, and operations teams in troubleshooting integration issues, reproducing data defects, clarifying expected behavior, and supporting root-cause analysis.
Help define operational monitoring, exception handling, restart/replay, auditability, and supportability requirements for production data workflows.
Ensure documentation is accurate, complete, and usable by engineering, QA, operations, implementation, and support teams.
Coordinate across cross-functional teams to manage dependencies, resolve impediments, support project schedules, and deliver high-quality data integration capabilities on time.
Provide clear communication to stakeholders regarding scope, requirements, risks, dependencies, decisions, trade-offs, and delivery progress.
Requirements
10+ years of business analysis, product analysis, or systems analysis experience within software delivery, data platforms, financial technology, or enterprise application development.
5+ years of hands-on experience supporting enterprise data integration, ETL/ELT, data warehousing, data migration, or data platform initiatives.
Strong financial services domain experience, preferably involving investment data, market data, reference data, positions, transactions, accounts, securities, operations data, or reporting/analytics workflows.
Strong understanding of enterprise data integration concepts, including ingestion, transformation, enrichment, validation, reconciliation, exception handling, data lineage, metadata, and downstream data consumption.
Experience producing source-to-target mapping documents, transformation specifications, data dictionaries, data flow diagrams, and detailed functional specifications.
Working knowledge of ETL/ELT tools and data integration patterns; experience with tools such as Talend, Informatica, Azure Data Factory/Fabric, Databricks, Qlik, Pentaho/Kettle, or similar platforms is valuable.
Experience with Snowflake and strong familiarity with relational database platforms such as Microsoft SQL Server and/or Oracle.
Strong SQL knowledge, including the ability to analyze queries, understand joins, interpret data models, validate data, and support root-cause analysis of data issues.
Familiarity with data warehouse concepts, dimensional modeling, staging layers, landing zones, transformation layers, warehouse/data mart consumption, and reporting dependencies.
Understanding of data governance, data quality, metadata management, data lineage, auditability, access controls, and regulatory/compliance considerations for financial data.
Experience working with batch, scheduled, file-based, API-based, and/or event-driven data integration workflows.
Ability to translate business needs into clear application requirements, user stories, acceptance criteria, data mapping rules, and functional specifications.
Strong analytical skills, including the ability to reconcile conflicting data, identify gaps, validate assumptions, and work through complex business and technical scenarios.
Strong communication and interpersonal skills, including the ability to facilitate discussions across business, product, architecture, engineering, QA, and operations teams.
Advanced writing skills with the ability to abstract, summarize, and clearly document highly detailed business, data, and technical information.
Demonstrated planning, prioritization, stakeholder management, and scheduling skills.
Strong Agile delivery experience, including Scrum ceremonies, Jira backlog management, sprint planning, story writing, acceptance criteria definition, and release planning.
Ability to independently ramp up on complex domains, drive clarity, proactively identify risks, and resolve impediments to maintain delivery momentum.
Ability to work effectively with globally distributed teams and stakeholders across multiple functional areas.
Experience supporting production issue analysis, defect triage, data validation, testing, and operational readiness is strongly preferred.
Experience with data modeling concepts, canonical data models, data stewardship, MDM/reference data, or enterprise-scale integration programs is preferred.
Knowledge of Charles River, State Street Alpha, investment management platforms, or financial data ecosystems is a strong plus.
Education
BS degree in Finance, Computer Science, Information Systems, Engineering, Mathematics, Data Science, Physics, or a related technical or quantitative field.
MS, MBA, or advanced degree strongly preferred.
About State Street
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