Data Architect – SnowflakeAdvance hands-on SQLResponsibilities- Technical Leadership: Provide technical direction and mentorship to a team of data engineers, ensuring best practices in coding, architecture, and data operations.- End-to-End Ownership: Architect, implement, and optimize end-to-end data pipelines that process and transform large-scale datasets efficiently and reliably.- Orchestration and Automation: Design scalable workflows using orchestration tools such as Apache Airflow, ensuring high availability and fault tolerance.- Data Warehouse and Lake Optimization: Lead the implementation and optimization of Snowflake and data lake technologies like Apache Iceberg for storage, query performance, and scalability.- Real-Time and Batch Processing: Build robust systems leveraging Kafka, SQS, or similar messaging technologies for real-time and batch data processing.- Cross-Functional Collaboration: Work closely with Data Science, Product, and Engineering teams to define data requirements and deliver actionable insights.- Data Governance and Security: Establish and enforce data governance frameworks, ensuring compliance with regulatory standards and maintaining data integrity.- Scalability and Performance: Develop strategies to optimize performance for systems processing terabytes of data daily while ensuring scalability.- Team Building: Foster a collaborative team environment, driving skill development, career growth, and continuous learning within the team.- Innovation and Continuous Improvement: Stay ahead of industry trends to evaluate and incorporate new tools, technologies, and methodologies into the organization.________________________________________QualificationsRequired Skills:- 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.- Experience working with cloud platforms like AWS, Azure, or GCP.Preferred Skills:- Proficiency in stream processing frameworks such as Apache Flink for real-time analytics.- Familiarity with programming languages like Scala or Java for additional engineering tasks.- Exposure to integrating data pipelines with machine learning workflows.- Strong analytical skills to evaluate new technologies and tools for scalability and performance.Leadership Skills:- Proven ability to lead and mentor data engineering teams, promoting collaboration and a culture of excellence.- Exceptional communication and interpersonal skills to articulate complex technical concepts to stakeholders.- Strategic thinking to align data engineering efforts with business goals and objectives.