Lead Data Engineer
Location : Hyderabad or Ahmedabad
Experience : 8+ years
Skills : Snowflake, Python / Pyspark, SQL
Only Immediate to 15 Days joiners apply.
Key Responsibilities :
- Lead the end-to-end Snowflake platform implementation, including architecture, design, data modeling, and governance.
- Oversee the migration of data and pipelines from legacy platforms to Snowflake, ensuring quality, reliability, and business continuity.
- Design and optimize Snowflake-specific data models, including use of clustering keys, materialized views, Streams, and Tasks.
- Build and manage scalable ELT / ETL pipelines using modern tools and best practices.
- Define and implement standards for Snowflake development, testing, and deployment, including CI / CD automation.
- Collaborate with cross-functional teams including data engineering, analytics, DevOps, and business stakeholders.
- Establish and enforce data security, privacy, and governance policies using Snowflake’s native capabilities.
- Monitor and tune system performance and cost efficiency through appropriate warehouse sizing and usage patterns.
- Lead code reviews, technical mentoring, and documentation for Snowflake-related processes.
- Directs technical quality efforts, including code reviews and performance tuning, to ensure the system meets high standards before deployment.
- Leads the technical planning for deployment, oversees the cutover process, and ensures the system is stable, monitored, and performant in the production environment.
Required Snowflake Expertise :
Snowflake Architecture – Deep understanding of virtual warehouses, data sharing, multi-cluster, zero-copy cloning.Performance Optimization – Proficient in tuning queries, clustering, caching, and workload management.Data Engineering – Experience with Snowpipe, Streams & Tasks, stored procedures (JavaScript-based), and data ingestion patterns.Data Security & Governance – Strong experience with RBAC, dynamic data masking, row-level security, and tagging.Advanced SQL – Expertise in complex SQL queries, transformations, semi-structured data handling (JSON, XML).Cloud Integration – Integration with major cloud platforms (AWS / GCP / Azure) and services like S3, Lambda, Step Functions, etc.Experience with ETL orchestration tools such as Airflow, DBT, and Matillion.Proficiency in handling semi-structured data formats, including JSON and Parquet.Familiarity with Git-based version control systems, including branching, merging, and pull request workflows.engineering.