Join a mission-driven fintech transforming access to financial services for small and medium-sized businesses across Latin America and beyond. As Data Engineering Tech Lead, you will own the architecture, technical strategy, and operational performance of a critical data platform supporting analytics, credit underwriting, and embedded financial services. You will combine hands-on engineering expertise with technical leadership, guiding a high-performing team while shaping the platform's long-term evolution. Working closely with senior leadership and cross-functional teams, you will drive scalable, secure, and cost-effective data solutions across multiple countries and regulatory environments. You will also play a key role in expanding the platform into Europe, including establishing an isolated EU data environment that meets strict data residency and compliance requirements. This opportunity offers significant ownership, progressive people management responsibilities, and the chance to influence the data foundations of a rapidly growing international fintech.
Data Platform Architecture and Technical Strategy: Own the end-to-end architecture of the data platform, covering data ingestion, cleansing, processing, warehousing, orchestration, and data flows supporting credit underwriting and analytics.
Technical Direction and Roadmap: Partner with the Director of Data Management to establish architectural principles, define engineering standards, and develop a technical roadmap that balances business priorities, platform reliability, modernization, and technical debt.
Technology and Vendor Management: Evaluate, select, and manage data engineering tools and vendors, contributing to technical assessments, purchasing decisions, and contract discussions.
Multi-Country Platform Architecture: Design and scale data infrastructure across multiple countries and legal entities, including a fully isolated European data platform with appropriate data residency and regional separation controls.
Hands-On Engineering: Remain actively involved in the most technically complex areas of the platform, particularly Snowflake architecture, Apache Airflow DAGs, dbt transformations, and Python-based data processing.
Production Reliability and Observability: Own the reliability, monitoring, observability, and service-level agreements of production data pipelines, ensuring data systems remain available, accurate, and performant.
Incident Management: Establish and maintain on-call rotations, incident response procedures, root cause analysis practices, and post-incident reviews to strengthen operational resilience.
Performance and Cost Optimization: Manage Snowflake infrastructure costs through warehouse sizing, consumption monitoring, budgeting, resource optimization, and data platform FinOps practices.
Engineering Standards and Quality: Define and enforce standards for data modeling, automated testing, continuous integration and deployment, documentation, and code reviews.
AI-Enabled Engineering: Identify and implement AI-powered tools and agents where they can demonstrably improve engineering productivity, data operations, and workflow efficiency.
Team Leadership and Delivery Management: Lead the Data Engineering team’s daily operations, including planning, prioritization, workload management, delivery schedules, and the resolution of technical blockers.
Project and Stakeholder Management: Drive data initiatives from initial scoping and estimation through execution and delivery, communicating timelines, dependencies, risks, and technical trade-offs to relevant stakeholders.
Mentoring and Team Development: Coach and develop Senior Data Engineers through architectural discussions, design reviews, code reviews, and ongoing feedback, progressively assuming direct people management responsibilities.
Recruitment and Organizational Growth: Participate in hiring, onboarding, and defining the team's structure as the organization expands.
Cross-Functional Collaboration: Partner with Analytics, Product, Risk and Credit, Machine Learning, Engineering, and Compliance teams to translate business needs into scalable data platform capabilities.
Data Governance and Quality: Co-own data governance with the Director of Data Management, ensuring appropriate data quality, lineage, cataloging, access controls, and documentation.
Privacy, Security, and Compliance: Design and enforce controls for personally identifiable information (PII), including classification, masking, encryption, role-based access, data retention, and deletion.
Regulatory Readiness: Ensure the platform meets applicable regulatory, audit, and reporting requirements, including GDPR, Brazil's LGPD, local central bank requirements, and relevant investor and business partner obligations.
Audit Documentation and Controls: Translate regulatory requirements into technical safeguards, documented processes, and verifiable evidence that support internal and external audits.