Data Engineer

Objective of the 

R

esponsible for designing, developing, and

maintaining

robust data pipelines and architectures that support the organization’s data-driven initiatives. This role contributes to all phases of the data engineering lifecycle, ensuring scalability, reliability, and performance.

D

emonstrates technical leadership by solving complex problems, mentoring junior engineers, and actively improving data engineering practices.

Main

• Design and implement scalable and efficient data pipelines for ingesting, processing, and storing structured and semi-structured data from diverse sources.

   

• Collaborate with stakeholders to gather requirements,

  identify

  data sources, and define the feasibility of

  requested

  solutions.

   

• D

  evelop and

  optimize

  ETL processes, ensuring the accuracy, consistency, and integrity of data throughout its lifecycle.

   

• Create and

  maintain

  data models, adhering to best practices for normalization and performance optimization.

   

• Build and manage cloud-based data architectures, including data lakes, data warehouses, and real-time streaming solutions.

   

• Implement monitoring and alerting systems to ensure the reliability and performance of data pipelines in production.

   

• Contribute to data governance initiatives by ensuring data quality, security, lineage, and compliance with relevant regulations.

   

• Utilize advanced data processing frameworks like Apache Spark, Apache Kafka, and Flink for batch and real-time data processing.

   

• Maintain and enhance CI/CD pipelines to automate data engineering workflows and ensure seamless deployment.

   

• Perform code reviews and enforce coding standards to ensure quality and maintainability.

   

• Document processes, architectures, and technical workflows to support knowledge sharing and operational continuity.

   

• Mentor junior engineers, sharing

  expertise

  and fostering a collaborative team environment.

   

• Identify

  opportunities for process optimization and automation to improve efficiency and reduce manual

  effort

  .

   

• Collaborate with cross-functional teams, including data scientists, analysts, and business stakeholders, to deliver impactful data solutions.

   

• 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 Experienc

  e

   

  • Minimum

    3

    - 4 years of experience as a Data Engineer

     

     

  • In-depth understanding of core data engineering concepts and principles, including complex ETL (Extract, Transform, Load) processes, scalable data pipelines, and advanced data warehousing techniques.

     

     

  • Advanced

    proficiency

    in Python, including writing efficient and optimized code, using advanced features like decorators, generators, and context managers.

     

     

  • Extensive experience with Python libraries and frameworks commonly used in data engineering, such as

    Python

    , NumPy,

    PySpark

    ,

    Pandas

    and

    Dask

    .

     

     

  • Strong knowledge of both SQL and NoSQL databases, including advanced querying, indexing, and optimization techniques.

     

     

  • Experience with database design, normalization, and performance tuning.

     

     

  • Advanced understanding of data modeling concepts and techniques, including star schema, snowflake schema, and dimensional modeling.

     

     

  • Experience with data modeling tools and best practices.

     

     

  • Proficient in various data processing methods, including batch processing, stream processing, and real-time data processing.

     

     

  • Extensive experience with data processing frameworks like Apache Spark, Apache Kafka, and Apache Flink.

     

     

  • Advanced knowledge of file processing concepts, including handling large

    datasets

    and working with different file formats (e.g., CSV, JSON, Parquet, Avro).

     

     

  • Strong understanding of data governance principles, including data quality management, data lineage, data security, and data privacy.

     

     

  • Comprehensive understanding of the end-to-end data engineering lifecycle, including data ingestion, transformation, storage, and retrieval.

     

     

  • Experience with CI/CD pipelines and automation for data engineering workflows.

     

     

  • Advanced understanding of various data architectures, including data lakes, data warehouses, data marts, and data mesh.

     

     

  • Experience designing and implementing scalable and robust data architectures.

     

     

  • Proficient with version control systems (e.g., Git) and experience managing code repositories on platforms like GitHub or GitLab.

     

     

  • Advanced understanding of data visualization tools (e.g.,

    Quicksight

    , L

    ooker Studio, Power Bi,

    Tableau) and reporting techniques.

     

     

  • Ability to create insightful and impactful visualizations and dashboards.

     

     

  • Strong understanding of cloud computing in

    Databricks,

    AWS and GCP stacks.

     

     

  • Basic experience with Infrastructure as Code (

    IaC

    ) tools like Terraform or CloudFormation.

     

     

  • Proven experience leading projects with Objectives and Key Results (OKRs),

    identifying

    risks, and delivering significant business value.

     

     

  • Ability to mentor and guide junior data engineers.

     

     

  • Strong ability to communicate project status transparently, including progress, challenges, and next steps.

     

     

  • Effective collaboration with cross-functional teams, including data scientists, analysts, and business stakeholders.

    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.