Data Architect - ETL & Snowflake DB

<p><p><br/><b>Key Responsibilities :</b></p><p><br/>- Design, develop, and maintain end-to-end ETL/ELT pipelines using Databricks (PySpark) and Snowflake to enable efficient and scalable data processing.<br/><br/></p><p>- Build and manage secure and scalable data platforms using PostgreSQL and DynamoDB, tailored to meet specific application and business requirements.<br/><br/></p><p>- Develop both real-time and batch data ingestion pipelines from a variety of sources including APIs, logs, files, and traditional databases.<br/><br/></p><p>- Apply complex transformation logic to clean, enrich, and normalize data, ensuring high data quality for analytics and reporting purposes.<br/><br/></p><p>- Optimize data pipelines and storage solutions by leveraging best practices such as partitioning, indexing, and query tuning to improve performance and reduce costs.<br/><br/></p><p>- Implement robust data governance frameworks including data quality checks, access controls, backup strategies, and </p><p>compliance policies.<br/><br/></p><p>- Lead the data strategy to unify and modernize large-scale existing data platforms and architectures.<br/><br/></p><p>- Establish and enforce data architecture principles, standards, and reusable design patterns to ensure consistency across teams and projects.<br/><br/></p><p>- Design and oversee enterprise data architectures including data lakes, data warehouses, and data mesh models.<br/><br/></p><p>- Collaborate closely with engineering teams to provide guidance on ingestion, transformation, storage strategies, and implement CI/CD best practices for production-grade data pipelines.<br/><br/><b>Required Skills & Qualifications :</b></p><p><br/>- 10+ years of proven experience in data architecture and engineering with a focus on ETL/ELT pipeline design and cloud data platforms.<br/><br/></p><p>- Hands-on expertise with Databricks (PySpark) and Snowflake for scalable data processing and warehousing.<br/><br/></p><p>- Strong knowledge of relational and NoSQL databases including PostgreSQL and DynamoDB.<br/><br/></p><p>- Experience building both real-time and batch ingestion pipelines from heterogeneous data sources.<br/><br/></p><p>- Proficient in data transformation, cleansing, and normalization techniques for analytical workloads.<br/><br/></p><p>- Deep understanding of performance tuning techniques such as partitioning, indexing, and query optimization.<br/><br/></p><p>- Experience implementing data quality frameworks, access controls, backup, and governance best practices.<br/><br/></p><p>- Demonstrated ability to lead data strategy and unify complex data environments.<br/><br/></p><p>- Expertise in designing and managing data lakes, warehouses, and emerging data architectures like data mesh.<br/><br/></p><p>- Familiarity with CI/CD pipelines, version control, and deployment best practices in data engineering contexts.<br/><br/></p><p>- Strong analytical, problem-solving, and communication skills with the ability to collaborate across technical and business teams.<br/><br/><b>Preferred Qualifications :</b></p><p><br/>- Experience working in agile environments and collaborating with cross-functional teams.<br/><br/></p><p>- Knowledge of cloud platforms and services related to data processing and storage (AWS, Azure, GCP).<br/><br/></p><p>- Prior experience in data governance and compliance frameworks (GDPR, HIPAA, etc.).</p><br/></p> (ref:hirist.tech)