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 production‑grade pipelines (batch and streaming), including transformation logic, data quality rules, lineage, and exception handling.

• Model‑ready data & MLOps enablement: Partner with data scientists to design feature pipelines, curate training and inference datasets, implement feature‑store 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 non‑prod/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, run‑books, and on‑call rotations; lead incident triage and root‑cause 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 run‑books; 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 metadata‑driven pipelines and self‑service 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 hands‑on 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 file‑based, 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 infrastructure‑as‑code.

• 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.

• Bachelor’s 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.

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