Requirements
- 3-5 years’ experience ‘hands-on’ as a member of a highly technical data-centric software engineers, preferably in financial services, in support of Data Lake houses (e.g. AWS, Databricks, Snowflake), data virtualizations solutions (e.g. Starburst), modern data catalogs & data quality capabilities (e.g. Collibra), and Master Data Management (e.g. Informatica)
- Proven AWS expertise (S3, EKS, Lambda, Glue, EMR, Redshift, S3, RDS/Aurora, Valkey/Redis, Step Functions, MSK/Kafka, Event Bridge).
- Experience applying AI/ML to boost engineering productivity (e.g., GitHub Copilot, Amazon Q).
- Strong Python for data engineering (Pandas, Spark/PySpark) and API Development (FastAPI or similar).
- Design and build high-performance REST/GraphQL APIs with optimized query patterns, pagination, caching, and schema design to ensure low-latency, performant reads at scale.
- Build ETL/ELT pipelines for batch and streaming; Kafka/MSK with schemas (Avro/JSON) and delivery semantics.
- Optimize performance of object-based and relational datastores
- Design applications with microservices and containerization mindset with ‘decoupling’ architectural principles
- Caching with Valkey/Redis: TTL strategies, invalidation, key design.
- CI/CD and IaC with GitHub, Git/GitHub workflows, ArgoCD.
- Security and reliability: IAM, KMS, secrets, VPC networking, observability (OpenTelemetry), SLOs, cost governance.
- Ability to solve complex data-driven requirements and triage towards defects and production bugs
- Technology solutions and tools including data governance and catalog solutions (e.g. Collibra, Alation, data platform and lake(house) (i.e. ‘medallion’ architecture), and data mesh and virtualization tools (e.g. Starburst, Denodo)
Nice to Have
- AWS Certification strongly preferred
- Hands-on experience building AI agents (e.g., using LangChain or LlamaIndex), implementing RAG over enterprise data, and enforcing robust guardrails through rigorous testing and evaluation.
- Applying AI/ML to boost engineering productivity (e.g., GitHub Copilot, Amazon Q) and using agentic AI to automate workflows such as code reviews, test generation, data quality checks, and runbook execution.
- Experience working with AI connection protocols (e.g., MCP, A2A) that ensure secure, interoperable, observable, and vendor-agnostic communication with authenticated, auditable messaging and robust error handling
- Experience with forming strong partnerships with stakeholders and product teams
- Great interpersonal and collaboration skills
- Ability to translate technical concepts to non-technical stakeholders.
Work Arrangement
Hybrid
Team
Structure: Fitch's Technology & Data Team is a dynamic department where innovation meets impact. Our team includes the Chief Data Office, Chief Software Office, Chief Technology Office, Emerging Technology, Shared Technology Services, Technology, Risk and the Executive Program Management Office (EPMO).
Additional Information
- Fitch is committed to providing global securities markets with objective, timely, independent and forward-looking credit opinions. To protect Fitch’s credibility and reputation, our employees must take every precaution to avoid conflicts of interest or any appearance of a conflict of interest. Should you be successful in the recruitment process at Fitch Ratings you will be asked to declare any securities holdings and other potential conflicts prior to commencing employment. If you, or your immediate family, have any holdings that may conflict with your work responsibilities, you may be asked to divest yourself of them before beginning work.
- Fitch is proud to be an Equal Opportunity and Affirmative Action Employer. We evaluate qualified applicants without regard to race, color, national origin, religion, sex, sexual orientation, gender identity, disability, protected veteran status, and other statuses protected by law.
