Senior Data Engineer - Snowflake
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What you'll do
- Building and maintaining large-scale data platforms and pipelines on Snowflake
- Data processing, transformation, and automation using Python and Spark/PySpark
- Automated code conversion and migration of legacy SQL, stored procedures, ETL scripts, and database objects to Snowflake
- Continuous data ingestion using Snowflake core capabilities
- Performance optimization of Snowflake components
- Implementing Snowflake security and governance features
- Production support, troubleshooting, and debugging of data pipelines
What they're looking for
- 4+ years of hands-on experience in data engineering, building and maintaining large-scale data platforms and pipelines on Snowflake
- Strong SQL expertise including complex analytical queries, window functions, stored procedures, and schema design
- Proficiency in Spark/PySpark and Python for data processing, transformation, and automation.
- Solid understanding of data modeling, schema design, partitioning strategies, and file formats (Parquet, ORC, Avro).
- Experience with batch and streaming data pipelines, ETL/ELT frameworks, and orchestration tools.
- Experience with Snowflake SnowConvert AI for automated code conversion and migration of legacy SQL, stored procedures, ETL scripts, and database objects from platforms such as Teradata, Oracle, Hive, or Spark to Snowflake-compatible SQL.
- Familiarity with multi-layer data architectures (Bronze/Silver/Gold medallion pattern)
- Experience with Snowflake core capabilities including Snowflake Procedures, UDFs, Streams, Tasks, Dynamic Tables, and Snowpipe for continuous data ingestion.
- Hands-on experience with Snowflake performance optimization — clustering keys, materialized views, query profiling, resource monitors, and warehouse sizing strategies.
- Working knowledge of Snowflake security and governance features — Role-Based Access Control (RBAC), data masking, row access policies, and tagging.
- Experience with Snowflake's data sharing and collaboration features including Secure Data Sharing, Snowflake Marketplace, and cross-region replication.
- Familiarity with Snowflake Cortex for AI/ML functions and Snowpark for building data pipelines in Python, Java, or Scala natively within Snowflake.
Nice to have
- Experience with Snowflake capabilities (Procedures, UDFs, Streams, Tasks, Cortex) and dbt for data transformation
- SQL expertise across legacy platforms such as Hive or Impala alongside Snowflake
- Experience with Snowpark or Snowflake migration tooling
- Familiarity with migration accelerators and remediation workflows
- Disciplined approach to data validation, reconciliation, and defect triage across source and target systems during migration
- Experience with cloud data platforms such as AWS (S3, Glue, Redshift, EMR, Lambda) and their integration with Snowflake
- Experience with CI/CD pipelines for data engineering (e.g., Jenkins, GitHub Actions, GitLab CI)
- Strong environment and dependency troubleshooting skills
Summarised by NextRaise from the employer’s description, which follows in full below.
Full description from employer
While technology is the heart of our business, a global and diverse culture is the heart of our success. We love our people and we take pride in catering them to a culture built on transparency, diversity, integrity, learning and growth.
If working in an environment that encourages you to innovate and excel, not just in professional but personal life, interests you- you would enjoy your career with Quantiphi!
Job Role : Senior Data Engineer (AWS + Snowflake Migrations)
Location : Mumbai/Bangalore
Experience : 4-7 Years
Must Have Skills
4+ years of hands-on experience in data engineering, building and maintaining large-scale data platforms and pipelines on Snowflake
Strong SQL expertise including complex analytical queries, window functions, stored procedures, and schema design
Proficiency in Spark/PySpark and Python for data processing, transformation, and automation.
Solid understanding of data modeling, schema design, partitioning strategies, and file formats (Parquet, ORC, Avro).
Experience with batch and streaming data pipelines, ETL/ELT frameworks, and orchestration tools.
Experience with Snowflake SnowConvert AI for automated code conversion and migration of legacy SQL, stored procedures, ETL scripts, and database objects from platforms such as Teradata, Oracle, Hive, or Spark to Snowflake-compatible SQL.
Familiarity with multi-layer data architectures (Bronze/Silver/Gold medallion pattern)
Experience with Snowflake core capabilities including Snowflake Procedures, UDFs, Streams, Tasks, Dynamic Tables, and Snowpipe for continuous data ingestion.
Hands-on experience with Snowflake performance optimization — clustering keys, materialized views, query profiling, resource monitors, and warehouse sizing strategies.
Working knowledge of Snowflake security and governance features — Role-Based Access Control (RBAC), data masking, row access policies, and tagging.
Experience with Snowflake's data sharing and collaboration features including Secure Data Sharing, Snowflake Marketplace, and cross-region replication.
Familiarity with Snowflake Cortex for AI/ML functions and Snowpark for building data pipelines in Python, Java, or Scala natively within Snowflake.
Knowledge of data warehousing concepts, dimensional modeling, and slowly changing dimensions.
Strong SDLC practices including Git version control, branching strategies, code reviews, and release management processes
Experience with monitoring, logging, alerting, and observability frameworks for data pipelines.
Strong troubleshooting, debugging, and production support capabilities.
Highly experienced in the use of AI / LLMs to accelerate data engineering work (e.g., Snowflake CoCo, Kiro, GitHub Copilot, Cursor, or similar GenAI-assisted development tools).
Excellent communication, problem-solving, and stakeholder management skills.
Snowflake SnowPro Core Certification required — advanced certifications a plus.
Good To Have Skills
Experience with Snowflake capabilities (Procedures, UDFs, Streams, Tasks, Cortex) and dbt for data transformation
SQL expertise across legacy platforms such as Hive or Impala alongside Snowflake
Experience with Snowpark or Snowflake migration tooling
Familiarity with migration accelerators and remediation workflows
Disciplined approach to data validation, reconciliation, and defect triage across source and target systems during migration
Experience with cloud data platforms such as AWS (S3, Glue, Redshift, EMR, Lambda) and their integration with Snowflake
Experience with CI/CD pipelines for data engineering (e.g., Jenkins, GitHub Actions, GitLab CI)
Strong environment and dependency troubleshooting skills
Experience with data pipelines and orchestration in hybrid or multi-cloud environments
Terraform or Infrastructure as Code (IaC) experience
Familiarity with Kafka, Iceberg, Delta Lake, or other modern streaming/lakehouse technologies
Understanding of data governance, data cataloging, and metadata management practices
If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us!
Company
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