[Hiring] Data Platform Engineer II @Spring Financial Inc.

Role Description

As a Data Platform Engineer II at Spring, you are a hands-on builder who delivers reliable, scalable data infrastructure. You'll work across our core stack of AWS, Snowflake, dbt, and Airflow to build pipelines and platform components that are secure, observable, and aligned with business needs. You'll own meaningful pieces of our data platform end to end: scoping the work, making the call on implementation, and shipping it. You'll make practical use of AI in your day-to-day engineering (e.g., for testing, schema discovery, and code generation) and contribute to AI-enabled features in the platform itself (e.g., automated QA, anomaly detection). Working with partners across Finance, Risk, Product, Analytics, and Engineering, you'll help clarify requirements, set and meet SLAs, and make sure the data you deliver is trustworthy.

What you’ll do:

• Platform & Pipeline Engineering
  • Build and maintain scalable, secure, and reliable data pipelines and platform components across AWS and Snowflake.
  • Help scope and deliver initiatives that modernize legacy data flows, bring together batch and streaming sources, and enable self-serve analytics across the organization.
  • Apply and help improve our engineering standards around testing, observability, security, and CI/CD within data systems.
• AI Integration
  • Help build AI capabilities into data pipelines (e.g., anomaly detection, automated tagging) and use AI in your own development practices (e.g., assisted testing, documentation).
  • Stay curious about how agentic and AI-driven workflows are changing data platform requirements, and bring what you learn back to the team.
• Cross-Functional Partnership
  • Work with engineers and business partners to clarify requirements, surface risks early, and propose practical solutions.
  • Partner with Analytics, ML, Finance, and other business teams to deliver data with the latency, accuracy, and governance their use cases need.
  • Communicate clearly about your work and its constraints, and be comfortable explaining technical trade-offs to non-technical partners.
• Craft & Growth
  • Contribute to technical design discussions and code reviews, and to the ongoing evolution of our data architecture.
  • Take ownership of the problems you pick up, holding a high bar for quality, documentation, and engineering craft, and seeing work through to production.
  • Sharpen your technical judgment through code review and design discussion with peers across data, software, and analytics engineering, and share what you know in return.

Qualifications

• Solid experience building data pipelines using Snowflake and AWS-native tools (e.g., Glue, Lambda, Redshift, Step Functions).
• Working experience with real-time data systems such as Kafka, Kinesis, Flink, or Spark Streaming.
• Good working knowledge of data modeling, schema evolution, and secure, privacy-conscious data design.
• Fluency in Python and SQL; some exposure to infrastructure-as-code (e.g., Terraform or CDK).
• Practical use of AI tools in your development workflow, and interest in building AI into the platform itself.
• Track record of delivering projects end to end, with an eye on the business value behind them.
• Strong communication and collaboration skills; a reliable partner to both engineering and business teams.
• Self-directed in ambiguity: comfortable asking good questions, acting on feedback, and taking on broader scope over time.

Nice to have

• Experience with dbt or analytics engineering patterns.
• Familiarity with ML platform tooling or feature store design.
• Background in fintech, credit risk, or other regulated data environments.

Benefits

• A competitive annual salary ranging from $612,000–$857,000 MXN, based on experience and impact.
• The flexibility to work fully remotely from anywhere in Mexico.
• A collaborative environment that supports learning, innovation, and professional growth.
• The opportunity to work on business-critical data infrastructure and platform initiatives with meaningful ownership and impact.
• Hands-on exposure to AWS, Snowflake, real-time data systems, and modern data engineering practices, with opportunities to explore and build AI-enabled capabilities.