Leads and supervises the data development team with Data Engineers. They are responsible for following the data strategy in the Business Unit where they are assigned. From a coordination and management perspective, they guide the development team under their charge, identify risks, and ensure that their team achieves Digital House's performance indicators. They drive innovation, collaborate diligently with other Data Leads to establish code quality standards, promote regulatory compliance, security, and foster a data-driven culture.
Responsabilities:
- 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.
Experience and Requirements:
- Minimum 6 years in Data Engineering or related fields, with at least 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.
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