ETL Data Architect

As a candidate for this role, you should have over 10 years of experience in designing and implementing scalable data solutions. This includes creating end-to-end ETL/ELT pipelines using Databricks (PySpark) and Snowflake. You will be responsible for building and managing secure, scalable data platforms using PostgreSQL and DynamoDB that are tailored to the needs of the applications. Your tasks will also involve developing real-time and batch ingestion pipelines from various sources like APIs, logs, files, and databases. Key Responsibilities: - Apply transformation logic to clean, enrich, and normalize data for analytics and reporting. - Optimize pipeline performance and storage costs by implementing best practices in partitioning, indexing, and query tuning. - Implement robust data quality checks, access controls, backup strategies, and governance policies. - Lead the data strategy and unify large-scale existing platforms. - Establish data architecture principles, standards, and patterns for consistent adoption. - Design and oversee data lakes, data warehouses, and data mesh architectures. - Guide engineering teams on ingestion, transformation, storage strategies, and CI/CD best practices in production environments. Qualifications Required: - At least 10 years of experience in designing and implementing scalable data solutions. - Proficiency in building ETL/ELT pipelines using Databricks (PySpark) and Snowflake. - Strong background in developing real-time and batch ingestion pipelines from diverse sources. - Experience in optimizing pipeline performance, storage costs, and implementing data quality checks. - Ability to lead data strategy and establish data architecture principles. - Familiarity with data lakes, data warehouses, and data mesh architectures. - Excellent communication skills to guide engineering teams on best practices. No additional details about the company were provided in the job description.,