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Data Architect - ETL & Snowflake DB

Data Architect - ETL & Snowflake DB

EduRunBangalore
26 days ago
Job description

Key Responsibilities :

  • Design, develop, and maintain end-to-end ETL / ELT pipelines using Databricks (PySpark) and Snowflake to enable efficient and scalable data processing.
  • Build and manage secure and scalable data platforms using PostgreSQL and DynamoDB, tailored to meet specific application and business requirements.
  • Develop both real-time and batch data ingestion pipelines from a variety of sources including APIs, logs, files, and traditional databases.
  • Apply complex transformation logic to clean, enrich, and normalize data, ensuring high data quality for analytics and reporting purposes.
  • Optimize data pipelines and storage solutions by leveraging best practices such as partitioning, indexing, and query tuning to improve performance and reduce costs.
  • Implement robust data governance frameworks including data quality checks, access controls, backup strategies, and

compliance policies.

  • Lead the data strategy to unify and modernize large-scale existing data platforms and architectures.
  • Establish and enforce data architecture principles, standards, and reusable design patterns to ensure consistency across teams and projects.
  • Design and oversee enterprise data architectures including data lakes, data warehouses, and data mesh models.
  • Collaborate closely with engineering teams to provide guidance on ingestion, transformation, storage strategies, and implement CI / CD best practices for production-grade data pipelines.

    • Required Skills & Qualifications :

  • 10+ years of proven experience in data architecture and engineering with a focus on ETL / ELT pipeline design and cloud data platforms.
  • Hands-on expertise with Databricks (PySpark) and Snowflake for scalable data processing and warehousing.
  • Strong knowledge of relational and NoSQL databases including PostgreSQL and DynamoDB.
  • Experience building both real-time and batch ingestion pipelines from heterogeneous data sources.
  • Proficient in data transformation, cleansing, and normalization techniques for analytical workloads.
  • Deep understanding of performance tuning techniques such as partitioning, indexing, and query optimization.
  • Experience implementing data quality frameworks, access controls, backup, and governance best practices.
  • Demonstrated ability to lead data strategy and unify complex data environments.
  • Expertise in designing and managing data lakes, warehouses, and emerging data architectures like data mesh.
  • Familiarity with CI / CD pipelines, version control, and deployment best practices in data engineering contexts.
  • Strong analytical, problem-solving, and communication skills with the ability to collaborate across technical and business teams.

    • Preferred Qualifications :

  • Experience working in agile environments and collaborating with cross-functional teams.
  • Knowledge of cloud platforms and services related to data processing and storage (AWS, Azure, GCP).
  • Prior experience in data governance and compliance frameworks (GDPR, HIPAA, etc.).

    • (ref : hirist.tech)

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