• Serve as the primary technical lead and escalation point for enterprise data engineering initiatives. • Bridge business requirements, architectural standards, and engineering implementation. • Partner with business analysts, architects, BI teams, DevOps, and data engineers to support successful solution delivery. • Interpret and clarify technical implementation requirements for data engineering teams. • Guide implementation decisions across Databricks pipelines, transformations, and data models. • Review engineering implementations for consistency, scalability, maintainability, and alignment to standards. • Support troubleshooting and root cause analysis for data quality issues, failed pipelines, performance concerns, and production defects. • Act as L1/L2 support lead for enterprise data platform operational issues. • Perform lineage and downstream impact analysis for data model and pipeline changes. • Guide implementation of reusable engineering patterns, medallion architecture, and gold-layer datasets. • Coordinate defect triage, release support, deployment validation, and production stabilization activities. • Support adoption of engineering standards, CI/CD processes, governance controls, and operational best practices. • Mentor and guide data engineers on technical implementation approaches and enterprise standards. • Drive consistency across engineering teams, platforms, and data products. • Document technical patterns, implementation standards, operational procedures, and support processes.

• Responsible for understanding, preparing, transforming, loading, and validating data migrated from the legacy system to the new model. • Map entities, fields, and relationships of the current model, including user, subscription, dependent, and payment where applicable to the scope. • Perform AS IS → TO BE mapping, identifying gaps, inconsistencies, duplicates, and required rules for the new data model. • Define and execute processes for extraction, cleansing, normalization, transformation, and loading of legacy Filó data. • Create import scripts, integrity controls, execution logs, volume validations, and data reconciliation. • Support modeling of the partner, company, beneficiary, dependents, offers, and subscriptions hierarchy. • Participate in cutover strategy, data freeze, migration window, and rollback planning.

• Define and implement Artificial Intelligence solutions applied to data modernization and legacy systems. • Develop mechanisms for analyzing, interpreting, and extracting technical information from legacy artifacts. • Build Generative AI–based solutions to accelerate documentation, transformation, and migration processes. • Create intermediate metadata models to represent flows, business rules, dependencies, entities, and transformations. • Develop accelerators and reusable components aligned with the enterprise data architecture. • Support the definition of templates, technical standards, and declarative structures for modern pipelines. • Develop and evolve pipelines using Databricks, PySpark, Lakeflow Jobs, and Declarative Pipelines. • Work with batch loads, incremental ingestions, CDC (Change Data Capture), and enterprise integrations. • Support the advancement of data governance, traceability, and data quality during migration. • Collaborate with architects, data engineers, and platform specialists to define scalable and secure solutions.

• Develop, design, and implement data warehousing solutions. • Collaborate with stakeholders to gather requirements • Perform data analysis and reporting to support decision-making. • Ensure data integrity and quality in data solutions.