Data Engineering Lead

Objective of the Role 

Oversees and mentors a team of Data Engineers, ensuring alignment with the data strategy for a Credit Fintech. This role combines technical leadership with people management, guiding the team to deliver high-quality data solutions that meet organizational OKRs and drive business value. Responsible for identifying and mitigating risks, setting standards for code quality and best practices, and fostering a secure, compliant, and data-driven culture. Will work closely with other Data Leads and business functions to innovate, enhance team capabilities, and establish cohesive data practices across the organization.   

Main Responsibilities 

• Understand business strategy, proposes and co-design Data Products and/or Solutions. Is responsible for conducting Understanding sessions to identify the list of ingestions and/or processes necessary to add value to the business.  

• Includes all involved areas in the Understanding stage to determine if the necessary inputs are available to address the requested requirements and to identify the viability of the Data Solution, as well as defining scoped scope to avoid rework due to incomplete definitions.  

• Supports data engineers in reviewing estimates based on their expert judgment considering timelines from all involved areas (architecture, security, SRE, data governance, etc.), including deployment, and ensures that deliveries are of quality, on time, and in form, avoiding rework.  

• Actively contributes to planning backlog tasks when a data project, product, and/or solution is authorized.  

• Provides a sprint status report of the project and/or data solution to the business where they are assigned and ensures that deliveries are on time, in form, and with quality.  

• Warns and informs the business of risks in a timely manner to mitigate them or propose contingencies.  

• Reviews the efforts of the data engineers under their charge and provides guidance in case the development does not meet standards, guidelines, or best practices.  

• Conducts rituals and weekly 1-to-1 follow-ups with the data engineers under their charge to assist them in case of doubts, to identify needs in meeting their performance, and to motivate adherence to best practices and compliance with guidelines.  

• Diligently and proactively contributes to all phases of the data engineering lifecycle with Agile methodology, avoiding reworks and delivering on time, in form, and with quality.  

• Analyzes and proposes technical solutions for data storage using best practices, standards, and data governance guidelines, data privacy strategies, security, and compliance.  

• Ensures the continuity of digital data solutions, insights, dashboards, etc., to build and consolidate a Data-Driven culture.  

• Collaborates and contributes to the development of monitoring processes and data quality metrics to ensure that the data used by the business is reliable, intact, and complete.  

• Promote an autonomous work culture by encouraging self-management, accountability, and proactive problem-solving among team members. 

• Serve as a Spin Culture Ambassador to foster and maintain a positive, inclusive, and dynamic work environment that aligns with the company's values and culture. 

Required Knowledge and Experience 

• Experience & Leadership: 7+ years in Data Engineering (or related fields), including 1–2 years of technical leadership, mentoring engineers, and managing complex end-to-end projects. 

• Data Lifecycle & Architecture: Expert in the Data Engineering Lifecycle (batch, micro-batch, and real-time processing), advanced solution design, dimensional modeling, and ETL/ELT frameworks across structured, semi-structured, and unstructured data (e.g., Parquet, JSON). 

• Cloud & Modern Data Stack: Proven expertise in AWS, Databricks, GCP, and cloud-native architectures to build, deploy, and scale data infrastructure. 

• Software Engineering & SDLC: Strong mastery of Python, SQL, and Java, alongside SDLC best practices, design patterns, version control (Git/GitHub/GitLab), and automated workflows. 

• Governance & Security: Extensive knowledge of data privacy, security, and governance standards to ensure compliance and data protection. 

• Communication & Collaboration: Strong ability to bridge technical and business domains, translating complex concepts for stakeholders while promoting code quality, standards, and continuous improvement across the team. 

• Experience working in the Credit Business is a plus. 
• Fluent English