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Senior Data Engineer

🇲🇽 Mexico

Kubernetes

Finance

Design

SQL

Analyst

Testing

Senior Data Engineer

from 🇲🇽 Mexico

The Opportunity

We are supporting a majordata platform transformation within a banking environment, moving from a legacy SQL Server and SSIS-based setup to a modern, scalable architecture built ondbt, Dagster, and OpenShift.

This role is not about maintaining existing systems. It is aboutrebuilding a critical data platform from the ground up, with direct impact onrisk, trading PnL, and core financial data flows.

We are looking for ahands-on Senior Data Engineer who can take ownership of complex migration workstreams and deliver reliably in a regulated, high-stakes environment.  

What You Will Do

You will play a central role in theend-to-end migration and modernisation of the data platform.

Platform Transformation

  • Translate legacy ETL logic from SSIS and stored procedures into modernELT pipelines using dbt
  • ImplementData Vault 2.0 structures including Raw Vault and Business Vault
  • Builddatamarts and curated datasets for downstream analytics and reporting


Orchestration & Infrastructure

  • Design and operate workflows usingDagster, including scheduling, dependencies, and recovery mechanisms
  • Deploy and run data workloads onOpenShift / Kubernetes environments


Event-Driven Data Processing

  • Enablenear real-time data processing using Kafka-triggered pipelines
  • Integrate with upstream data lake environments and external data providers


Data Quality & Validation

  • Establish robustdata validation and reconciliation processes
  • Implement automated testing and monitoring using dbt


Operational Ownership

  • Support production pipelines and resolve incidents when required
  • Create clear documentation and ensure operational readiness
  • Continuously improve performance, reliability, and maintainability

What You Will Do

You will play a central role in theend-to-end migration and modernisation of the data platform.


Platform Transformation

  • Translate legacy ETL logic from SSIS and stored procedures into modernELT pipelines using dbt
  • ImplementData Vault 2.0 structures including Raw Vault and Business Vault
  • Builddatamarts and curated datasets for downstream analytics and reporting


Orchestration & Infrastructure

  • Design and operate workflows usingDagster, including scheduling, dependencies, and recovery mechanisms
  • Deploy and run data workloads onOpenShift / Kubernetes environments


Event-Driven Data Processing

  • Enablenear real-time data processing using Kafka-triggered pipelines
  • Integrate with upstream data lake environments and external data providers


Data Quality & Validation

  • Establish robustdata validation and reconciliation processes
  • Implement automated testing and monitoring using dbt


Operational Ownership

  • Support production pipelines and resolve incidents when required
  • Create clear documentation and ensure operational readiness
  • Continuously improve performance, reliability, and maintainability

What You Bring

Technical Expertise

  • Strong experience withSQL Server and T-SQL, including performance optimisation
  • Proven hands-on experience withdbt in production environments
  • Solid experience withworkflow orchestration tools, ideally Dagster
  • Practical knowledge ofData Vault 2.0 modelling concepts
  • Experience working withcontainer platforms such as OpenShift or Kubernetes
  • Familiarity withevent-driven architectures and Kafka


Domain Experience

  • Experience working withfinancial data, ideally in banking or trading environments
  • Understanding ofrisk and PnL data structures is a strong advantage


Working Style

  • Strong ownership mindset with the ability to work independently
  • Structured, pragmatic, and delivery-focused
  • Comfortable operating in complex and regulated environments
  • Clear communicator across both technical and business stakeholders

What Success Looks Like

Within the first months, you will have:

  • Delivered initialData Vault structures and migrated datasets into the new platform
  • Establishedstable, event-driven pipelines
  • Ensureddata consistency and validation between legacy and new systems
  • Contributed to aproduction-ready, scalable data platform
by @maxrusakovic