Data Engineering Tech Lead
Accountabilities: 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.
Requirements:
Professional Experience: 8–12+ years of experience in Data Engineering or a closely related discipline, with a demonstrated record of delivering and operating complex production data platforms.
Technical Leadership: Previous experience as a Principal or Staff Data Engineer, or as a Tech Lead managing or guiding a team of at least four engineers.
Team and Project Management: Proven ability to lead team delivery, manage multi-quarter initiatives, prioritize competing demands, coordinate stakeholders, and communicate project risks and trade-offs effectively.
Advanced Snowflake Expertise: Extensive hands-on production experience with Snowflake, including architecture design, performance tuning, cost optimization, warehouse management, and role-based access control. Snowflake experience is mandatory. Candidates whose experience is primarily with Databricks or BigQuery without substantial Snowflake expertise will not be a fit.
Airflow and dbt: Advanced production experience with Apache Airflow for workflow orchestration and dbt for data transformation, modeling, and testing.
Software Engineering and Programming: Strong software engineering foundations, advanced Python proficiency, and excellent SQL skills, including experience with dimensional modeling and analytical data architecture.
Data Integration and ELT: Practical experience implementing ELT patterns and using data ingestion and integration tools such as Airbyte or Fivetran.
Cloud Infrastructure: Experience designing, deploying, and operating data platforms on AWS, including cloud infrastructure management and optimization.
Multi-Region and Regulatory Architecture: Demonstrated experience building or operating data platforms across multiple countries, legal entities, or regulatory jurisdictions. This is a mandatory requirement.
Data Privacy and Governance: Practical knowledge of data protection regulations such as GDPR and LGPD, with experience implementing governance frameworks and technical controls for sensitive and personally identifiable information.
Production Operations: Comfort owning business-critical production systems, including monitoring, alerting, incident response, troubleshooting, and continuous reliability improvements.
Stakeholder Communication: Ability to explain complex technical decisions and trade-offs clearly to senior leadership and non-technical partners in Risk, Product, and Compliance.
Language Skills: Professional proficiency in English, together with Spanish or Portuguese, to collaborate effectively across international teams.
Leadership and Collaboration: A proactive, hands-on approach to technical problem-solving, with strong mentoring, organizational, communication, and decision-making skills.
Preferred Fintech Experience: Background in fintech, lending, credit risk, or data platforms supporting underwriting and financial services.
Preferred Regional Platform Experience: Experience establishing an EU data platform or another isolated regional environment with strict data residency requirements.
Preferred Governance Tooling: Familiarity with data cataloging, lineage management, and data contract tools.
Preferred AI and Machine Learning Experience: Experience integrating AI/ML tools or agents into data workflows, or developing feature pipelines for machine learning models.
Preferred Analytics Enablement: Experience designing self-service analytics platforms that enable business teams to access and use data efficiently.
Preferred FinOps Expertise: Experience managing cloud data infrastructure costs and implementing cost optimization practices at scale.
Benefits:
Mission-Driven Work: Contribute to a fintech initiative focused on expanding access to financial solutions for small and medium-sized businesses.
Significant Technical Ownership: Shape the architecture, technical roadmap, tooling strategy, and operational performance of a business-critical data platform.
Leadership Development: Progress from hands-on technical leadership into broader team management, including mentoring, performance development, and organizational planning.
International Exposure: Help expand data infrastructure across Latin America and Europe while addressing complex multi-country architecture and regulatory challenges.
Cross-Functional Collaboration: Work closely with experienced professionals across Data, Engineering, Product, Risk, Credit, Machine Learning, and Compliance.
Innovative Technology Environment: Work with modern data technologies, cloud infrastructure, automation, and AI-enabled engineering tools.
High-Growth Environment: Join a fast-paced organization where technical decisions can have a direct impact on business performance and long-term scalability.
Ownership-Oriented Culture: Eligible team members may participate in a performance-based Phantom Share Program designed to align contributions with long-term business success and value creation.
Professional Growth: Gain exposure to complex fintech challenges, embedded finance, credit underwriting, data governance, and international platform expansion.
Remote Work: Work remotely from eligible locations in Latin America, including Brazil, Argentina, Colombia, and Chile.
How Jobgether works: We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team. We appreciate your interest and wish you the best! Why Apply Through Jobgether?
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