Our client is seeking a Data Engineer based in Monterrey, Mexico. The team builds and maintains data pipelines, ETL processes, and data infrastructure supporting integral financial data products.

As a leading, global financial information services provider, our client delivers vital credit and risk insights, robust data, and dynamic tools to champion more efficient, transparent financial markets. With over 100 years of experience and colleagues in over 30 countries, our client's culture of credibility, independence, and transparency is embedded throughout its structure, which includes our client Ratings, one of the world's top three credit ratings agencies, and our client Solutions, a leading provider of insights, data and analytics. With dual headquarters in London and New York, our client is owned by Hearst.

Our client'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). Driven by our investment in cutting-edge technologies like AI and cloud solutions, we're home to a diverse range of roles and backgrounds united by a shared passion for leveraging modern technology to drive projects that matter to our organization and clients. We are also proud to be recognized by Built In as a "Best Place to Work in Technology" 3 years in a row. Whether you're an experienced professional or just starting your career, we offer an exciting and supportive environment where you can grow, innovate, and make a difference.

How You'll Make an Impact

- 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.

You May Be a Good Fit

- 3-5 years' experience 'hands‐on' as a member of a highly technical data‐centric software engineering team, preferably in financial services, in support of Data Lake houses (e.g. AWS, Databricks, Snowflake), data virtualization 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, RDS/Aurora, Valkey/Redis, Step Functions, MSK/Kafka, Event Bridge).




- AWS Certification strongly preferred.
- 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 a 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's architecture), and data mesh and virtualization tools (e.g., Starburst, Denodo).

What Would Make You Stand Out

- 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 generat

📌 Data Engineer (Monterrey)
🏢 HopHR
📍 Monterrey