Senior Data Architect

hace 6 días

Ciudad de México, Ciudad de México GSPANN Technologies, Inc A tiempo completo
About the Role

GSPANN Technologies, Inc. is seeking a highly skilled Data Architect to lead and define data architecture, ensure data quality, and establish data governance processes.

Key Responsibilities
  • Design and Implement Data Architecture: Develop and lead data architecture, ensuring high standards of data quality and governance across the organization.
  • Establish Data Modeling Standards: Create and promote data modeling standards and best practices.
  • Develop Data Quality Standards: Develop and advocate for data quality standards and practices.
  • Implement Data Governance Processes: Create and maintain data governance processes, procedures, policies, and guidelines to ensure data integrity and security.
  • Promote Data Utilization: Promote the successful adoption of data utilization and self-service data platforms within the organization.
  • Develop Critical Data Standards: Create and maintain critical data standards and metadata to enable data as a shared asset.
  • Design Data Schemes and Object Models: Design data schemes, object models, and flow diagrams to structure, store, process, and integrate data.
  • Provide Architectural Assessments: Provide architectural assessments, strategies, and roadmaps for data management.
  • Implement Industry Best Practices: Implement and manage industry best practice tools and processes, including Data Lake, Databricks, Delta Lake, S3, Spark ETL, Airflow, Hive Catalog, Redshift, Kafka, Kubernetes, Docker, and CI/CD pipelines.
  • Translate Big Data Requirements: Translate big data and analytics requirements into scalable and high-performance data models, guiding data analytics engineers.
  • Define Templates and Processes: Define templates and processes for designing and analyzing data models, data flows, and integration.
  • Lead and Mentor Data Analytics Team Members: Lead and mentor Data Analytics team members in best practices, processes, and technologies in Data Platforms.
Requirements
  • Bachelor's or Master's Degree: Bachelor's or Master's degree in Computer Science or a related field.
  • Hands-on Experience: Over 10 years of hands-on experience in Data Warehousing, ETL processes, Data Modeling, and Reporting.
  • Big Data Experience: More than 7 years of experience in productizing and deploying Big Data platforms and applications.
  • Technical Expertise: Expertise in relational/SQL, distributed columnar data stores/NoSQL databases, time-series databases, Spark streaming, Kafka, Hive, Delta Parquet, Avro, and more.
  • Subject-Matter Expertise: Hands-on subject-matter expertise in the architecture and administration of Big Data platforms and Data Lake Technologies (AWS S3/Hive), and experience with ML and Data Science platforms.
  • Complex Business Use Cases: Extensive experience in understanding complex business use cases and modeling data in the data warehouse.
  • Technical Skills: Proficiency in SQL, Python, Spark, AWS S3, Hive data catalog, Parquet, Redshift, Airflow, and Tableau or similar tools.
  • Custom Enterprise Data Warehouses: Proven experience in building Custom Enterprise Data Warehouses or implementing tools like Data Catalogs, Spark, Tableau, Kubernetes, and Docker.
  • Infrastructure Requirements: Good knowledge of infrastructure requirements such as Networking, Storage, and Hardware Optimization, with hands-on experience with Amazon Web Services (AWS).
  • Communication Skills: Strong verbal and written communication skills, with the ability to work efficiently across internal and external organizations and virtual teams.
  • Industry Leadership: Demonstrated industry leadership in Data Warehousing, Data Science, and Big Data technologies.
  • Distributed Systems: Strong understanding of distributed systems and container-based development using Docker and the Kubernetes ecosystem.
  • Data Structures and Algorithms: Deep knowledge of data structures and algorithms.
  • Agile Methodologies: Experience working in large teams using CI/CD and agile methodologies.

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