Main Responsibilities
• Lead the design and implementation of robust and scalable data pipelines, enabling efficient ingestion, processing, and storage of structured, semi-structured, and unstructured data.
• Collaborate with cross-functional teams to gather requirements, define technical solutions, and align data architectures with business needs.
• Develop and optimize data models using industry-standard techniques to support analytics, reporting, and AI-driven solutions.
• Implement advanced data processing workflows, including batch, microbatch, near real-time, and realtime pipelines, to ensure seamless data availability and performance.
• Drive the adoption of data governance and privacy strategies, ensuring compliance with security and regulatory requirements throughout the data lifecycle.
• Design and maintain data architectures, contributing to the scalability and reliability of data lakes, warehouses, and real-time streaming platforms.
• Develop monitoring processes and data quality metrics to ensure the reliability, integrity, and completeness of data used for decision-making.
• Actively contribute to all phases of the data engineering lifecycle, adhering to Agile methodologies and ensuring timely delivery of high-quality solutions.
• Mentor and guide junior and mid-level engineers, fostering a collaborative and inclusive team environment.
• Maintain and document data architectures, processes, and technical workflows to ensure continuity and knowledge sharing.
• Lead the integration of APIs and external data sources, optimizing data flows and ensuring compatibility with existing architectures.
• Manage version control and repository practices to maintain clean, reliable, and maintainable codebases.
• 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.