Chief Data Solutions Architect

Há 6 dias

Brasília DF, Brasil beBeeDataLeadEngineer Tempo inteiro

Job Title The role of Data Lead Engineer involves providing technical direction and mentorship to a team of data engineers, ensuring best practices in coding, architecture, and data operations. As a key member of the engineering team, you will be responsible for architecting, implementing, and optimizing end-to-end data pipelines that process and transform large-scale datasets efficiently and reliably. Key skills required for this position include strong programming skills in Python, expertise in building and optimizing ETL pipelines, and extensive experience with Snowflake or equivalent data warehouses. You will also have a deep understanding of messaging queues such as Kafka, AWS SQS, or similar technologies for real-time data ingestion and processing, demonstrated ability to architect and implement scalable data solutions handling terabytes of data, hands-on experience with Apache Iceberg for managing and optimizing data lakes, and proficiency in containerization and orchestration tools like Docker and Kubernetes. Responsibilities Technical Leadership: Provide technical direction and mentorship to a team of data engineers. End-to-End Ownership: Architect, implement, and optimize end-to-end data pipelines. Orchestration and Automation: Design scalable workflows using orchestration tools. Data Warehouse and Lake Optimization: Lead the implementation and optimization of Snowflake and data lake technologies. Real-Time and Batch Processing: Build robust systems leveraging Kafka, SQS, or similar messaging technologies. Cross-Functional Collaboration: Work closely with Data Science, Product, and Engineering teams. Data Governance and Security: Establish and enforce data governance frameworks. Scalability and Performance: Develop strategies to optimize performance for systems processing terabytes of data daily. Qualifications 8+ years of experience in data engineering with a proven track record of leading data projects or teams. Strong programming skills in Python, with expertise in building and optimizing ETL pipelines. Extensive experience with Snowflake or equivalent data warehouses for designing schemas, optimizing queries, and managing large datasets. Expertise in orchestration tools like Apache Airflow, with experience in building and managing complex workflows. Deep understanding of messaging queues such as Kafka, AWS SQS, or similar technologies for real-time data ingestion and processing. Demonstrated ability to architect and implement scalable data solutions handling terabytes of data. Hands-on experience with Apache Iceberg for managing and optimizing data lakes. Proficiency in containerization and orchestration tools like Docker and Kubernetes for deploying and managing distributed systems. Strong understanding of CI/CD pipelines, including version control, deployment strategies, and automated testing. Proven experience working in an Agile development environment and managing cross-functional team interactions. Strong background in data modeling, data governance, and ensuring compliance with data security standards.

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