Data Engineer

We're hiring for Snowflake Data Engineer with Infojini Job Summary We are seeking a highly skilled Snowflake Data Engineer to design, develop, and optimize scalable data pipelines. The ideal candidate will leverage Snowflake’s cloud-native features alongside Python and PySpark to handle complex data transformations and large-scale data integration. You will be instrumental in migrating legacy data workloads to the cloud and ensuring high-performance data delivery for analytics and AI/ML initiatives. ***Immediate joiners only*** Key Responsibilities Pipeline Development: Design and implement end-to-end ELT/ETL pipelines to ingest data from diverse sources (APIs, IoT streams, S3/Azure Blobs, On-premise databases) into Snowflake.

Data Transformation: Utilize PySpark for heavy-duty distributed processing and Python (Snowpark) for procedural logic and data manipulation within the Snowflake environment. Snowflake Optimization: Manage and optimize Snowflake objects including Virtual Warehouses, Stages, Pipes (Snowpipe), Streams, and Tasks for cost and performance. Advanced Scripting: Develop complex SQL queries, Stored Procedures (Python/SQL), and User Defined Functions (UDFs) to support business logic.

Performance Tuning: Use Query Profiling to identify bottlenecks and implement strategies like Clustering Keys and Search Optimization Service. Data Modeling: Design scalable data models (Star/Snowflake schema) and implement Data Vault or Medallion (Bronze/Silver/Gold) architectures. Security & Governance: Implement Role-Based Access Control (RBAC), data masking, and row-level security to ensure compliance with GDPR/CCPA.

Technical Requirements: Cloud Warehouse: Deep expertise in Snowflake (Snowpipe, Tasks, Streams, Zero-Copy Cloning, Time Travel). Programming: Advanced Python and SQL. Ability to write clean, PEP8-compliant code.

Big Data: Proficiency in PySpark (Spark Core, Spark SQL, DataFrames) for large-scale data processing. Frameworks: Experience with Snowpark and dbt (data build tool) for modular SQL development. Orchestration: Familiarity with tools like Apache Airflow, Prefect, or Dagster.

Infrastructure: Hands-on experience with at least one cloud provider (AWS, Azure, or GCP). DevOps: Version control with Git, CI/CD pipelines, and automated testing (Pytest). Experience & Qualifications Education: Bachelor’s or Master’s degree in Computer Science, Data Engineering, or a related field.

Professional Experience: 3+ years of experience in data engineering, with at least 1-2 years focused specifically on the Snowflake ecosystem. Certifications (Preferred): SnowPro Core or SnowPro Advanced Data Engineer; Databricks/Spark Certified Developer Interested candidates share their updated resume