Job description:

Role Description:

We are recruiting a VP-level Data Scientist and ETL Developer to design, build, and operate robust data integration and analytics pipelines that power State Streets risk analytics and reporting products. The role focuses on extending our data transformation and delivery architecture to onboard new client and vendor data sources, standardize them to enterprise data models, and deliver highquality, timely information to downstream risk, regulatory, and management reporting platforms. In addition to integration, the role will lead advanced data preparation for modelready datasets, support feature engineering, and enable model deployment and monitoring in partnership with data science teams.

Function:

This role sits within Risk Services and works closely with product managers and engineering leads to:

1. execute the risk data integration roadmap,

2. modernize legacy data movements to eventdriven/streaming and cloud patterns, and

3. enable seamless migration of clients from legacy to targetstate architectures while maintaining regulatory and control standards.

Responsibilities:

- Design & build data integrations: Develop resilient ingestion, mapping, validation, and publishing processes to bring client and market data from multiple custodians and vendors into standardized schemas supporting risk analytics and reporting.

- Own ETL/ELT workflows: Translate business and data analysis into productiongrade pipelines (batch and streaming), including transformation logic, data quality rules, lineage, and exception handling.

- Modelready data & MLOps enablement: Partner with data scientists to design feature pipelines, curate training and inference datasets, implement featurestore patterns, and operationalize model scoring and monitoring.

- Integration patterns & architecture: Contribute to capability models and reference patterns (API, file, message/stream, CDC) that simplify and standardize integration across risk platforms; document and review designs.

- Environment readiness & releases: Automate build, test, and deploy processes; ensure nonprod/prod environments, secrets, and dependencies are correctly configured; support blue/green and canary releases where applicable.

- Production reliability: Partner with production support to implement monitoring, alerting, runbooks, and oncall rotations; lead incident triage and rootcause analysis; continuously harden pipelines for resiliency and cost.

- Data quality & controls: Implement reconciliation, validation, and auditability controls aligned to internal policies and external regulations for risk data.

- Stakeholder engagement: Work with product managers, client service, and operations to prioritize backlog, groom user stories, and align technical plans with client deliverables and regulatory deadlines.

- Documentation & knowledge transfer: Produce clear technical specifications, mappings, and runbooks; coach junior team members and enable handoffs to global support teams.

- Continuous improvement: Identify opportunities to rationalize tech stacks, retire redundant feeds, and evolve toward metadatadriven pipelines and selfservice data delivery.

- Adaptability & Continuous Learning: The ability to keep up with the fast-paced, evolving AI landscape.

- Critical Thinking & Evaluation: The ability to verify AI outputs, check for hallucinations, and identify bias.

Skills (What we're looking for):

Essential:

- Strong handson experience building ETL/ELT pipelines and data mappings for financial services, ideally in risk, performance, or regulatory reporting contexts.

- Proficiency with SQL (SQL Server/Oracle), Python/Scala, and a workflow/orchestration tools.

- Integration patterns across filebased, API, and message/stream (Kafka/Event Hubs); comfort with schema/version management, idempotency, and backfills.

- Data modelling & quality: dimensional/relational modelling, DQ rules, reconciliation, lineage/metadata cataloguing.

- Applied data science skills: feature engineering, model evaluation metrics, and experience supporting model deployment/monitoring with MLOps practices.

- Agile delivery (stories, epics, backlogs), CI/CD, and modern git workflows; clear written/spoken communication across global teams.

Desired:

- Reporting/visualization exposure (e.g., SSRS/Power BI) and experience modernizing or decommissioning legacy report stacks.

- Experience with cloud platforms (Azure/AWS), object storage, Spark/Databricks, dbt, and infrastructureascode.

- Familiarity with enterprise controls for financial services (change management, access, segregation of duties) and regulatory reporting data needs.

- Experience with feature stores, model registries, and monitoring (e.g., MLflow, Feast, EvidentlyAI) is a plus.

Experience:

- 10+ years total experience in data integration / data engineering / data science, with at least 5+ years building production pipelines for financial data.

- 3+ years leading technical delivery or small teams, including code reviews, standards, and mentoring.

- Bachelors degree in computer science, Engineering, Information Systems, Mathematics or related field; advanced degree is a plus.

- Demonstrated success delivering change in complex global environments using Agile and/or hybrid models.

Split of role:

- Design & development: 50%

- Production reliability & support enablement: 15%

- Analysis, documentation, and testing: 15%

- Stakeholder management & planning: 20%

Key relationships:

- Internal: Product managers (Risk Services), Data Engineering, Production Support, Client Service/Operations (prioritization and delivery).

- External: Data vendors and technology partners as needed for onboarding and API/format changes.