Data Engineering Lead (Data Sharing)

Job description

Join to apply for the Data Engineering Lead (Data Sharing) role at Spin

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Join to apply for the Data Engineering Lead (Data Sharing) role at Spin

Ensure alignment with the data strategy of their assigned Data Project.

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 to innovate, and Business Roles, enhance team capabilities, and establish cohesive data practices across the organization.
Main Responsibilities

• Receives requirements for Data Products and/or Solutions and is responsible for conducting Understanding sessions to identify the list of ingestions and/or processes necessary to add value to the business where they are assigned.
  
  
• 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

• Minimum 7 years in Data Engineering or related fields, with at least 1-2 years in a technical leadership role overseeing and mentoring Data Engineers.
  
  Demonstrates experience in managing complex projects, coordinating team efforts, and ensuring alignment with organizational goals.
  
  
• Applies expert understanding of the Data Engineering Lifecycle, with proficiency in data processing techniques across batch, micro-batch, near real-time, and real-time data solutions.
  
  
• Brings advanced knowledge in cloud computing environments, with proven expertise in using Google Cloud Platform (GCP) and Amazon Web Services (AWS) data stacks to build, deploy, and optimize scalable data infrastructure.
  
  
• Demonstrates strong skills in software development life cycle (SDLC) methodologies and design patterns, ensuring code quality and maintainability.
  
  
• Possesses advanced skills in data architecture and solution design, with the ability to develop and maintain data models and architectures that align with business needs and strategic objectives.
  
  
• Uses extensive knowledge of data privacy, security, and governance best practices to implement and maintain robust compliance and data protection measures.
  
  
• Proficient in dimensional data modeling, ETL/ELT frameworks, and storage solutions for structured, semi-structured, and unstructured data, including handling formats like CSV, JSON, Parquet, and Yaml.
  
  
• Exhibits advanced expertise in Python, Java, and SQL, along with hands-on experience in data frameworks and tools for processing, analyzing, and optimizing data workflows.
  
  
• Skilled in version control systems (Git, GitHub, GitLab), ensuring team collaboration and project continuity across data engineering projects.
  
  
• Communicates effectively with both technical and business stakeholders, translating complex technical concepts into actionable insights and ensuring alignment on project objectives.
  
  
• Fosters a collaborative and data-driven culture by guiding team members on best practices, setting code quality standards, and driving continuous improvement in data engineering methodologies.
  

Spin está comprometida con un lugar de trabajo diverso e inclusivo.
Somos un empleador que ofrece igualdad de oportunidades y no discrimina por motivos de raza, origen nacional, género, identidad de género, orientación sexual, discapacidad, edad u otra condición legalmente protegida.
Si desea solicitar una adaptación, notifique a su Reclutador.

Seniority level

• Seniority level Not Applicable

Employment type

• Employment type Full-time

Job function

• Job function Information Technology

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