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# Senior Software Engineer

- **Company:** Fitch Group, Inc.
- **Location:** New York, NY, United States
- **Experience:** Expert
- **Salary:** $135,000.0 - $145,000.0
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Amazon S3, Data Analysis, Cloud Engineering, Code Review, Databases, Continuous Integration, Data Validation, Information Engineering, Data Governance, Data Integration, Elasticsearch, Github, Interoperability, Performance Tuning, Redis, Cloud Services, Systems Integration, Data Ingestion, GitHub Copilot, Retrieval-Augmented Generation, Apache Spark, Spring-boot, Llamaindex, Caching, Generative AI, Backend, Agentic-AI, Fastapi, Pandas, Pyspark, Apache Kafka, Graphql, Data Management, Api Design, Software Coding, Restful APIs, Pagination, OpenSearch, Agent2Agent Protocol
- **Published:** October 27, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=137df74d1d103d52

## About the Role

* Hands-on Solution Archtiect with 8+ years of experience in Data Management, expertise in designing, building, and governing enterprise-scale data management solutions that enable high-quality, trusted, and accessible data across the organization. This role combines strategic architecture leadership with hands-on technical implementation across Master Data Management (MDM e.g. Informatica), Data Governance, Data Integration, Data Quality, APIs, Cloud Data Platforms, and Analytics ecosystems.
 * Proven AWS expertise (S3, EKS, Lambda, Glue, EMR, Redshift, S3, RDS/Aurora, Valkey/Redis, Step Functions, MSK/Kafka, Event Bridge), AWS Certification strongly preferred
 * Experience applying AI/ML to boost engineering productivity (GitHub Copilot, Amazon Q).
 * In-depth experience in Python for data engineering (Pandas, Spark/PySpark) and API Development (FastAPI or similar).

 * Lead the design and development of mission-critical backend services leveraging Java, Micoservice architecture, Spring Boot, REST APIs, GraphQL, and cloud-native architectures, while promoting coding standards, performance optimization, and best practices.

 * Experience in optimized query patterns, pagination, caching, and schema design to ensure low-latency, performant reads at scale.
 * Experince in in-memory database e.g. Valkey, Elastic Search, Redis, OpenSearch
 * Expericen is CI/CD,GitHub, Git/GitHub workflows, ArgoCD.
 * Ability to solve complex data-driven requirements and triage towards defects and production bugs, * 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
 * Caching with Valkey/Redis: TTL strategies, invalidation, key design.
 * Experience with forming strong partnerships with stakeholders and product teams
 * Great interpersonal and collaboration skills
 * Ability to translate technical concepts to non-technical stakeholders.

## Description

* Contribute as a member of a high-performing data engineering team, by implementing modern scalable data solutions aligned with business priorities.
 * Partner with the CDO team and data product leaders to implement the vision for an AWS-based data platform that enables enterprise-wide self-service, unlocking data for AI, analytics, and innovation.

 * Implement architectural decisions and establish best practices for data ingestion, transformation, and distribution frameworks that accelerate time-to-insight for data producers and consumers.

 * Champion adoption of emerging technologies such as Agentic AI and Generative AI, integrating advanced capabilities into data workflows to enhance user experience and position the organization as "AI-Ready."
 * Ensure operational excellence and reliability by proactively addressing complex data engineering challenges, implementing robust quality controls, and preventing production incidents.