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
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.
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!
Skills Required
- 4+ years of hands-on data engineering experience 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 or PySpark and Python for data processing, transformation, and automation
- Understanding of data modeling, schema design, partitioning strategies, and Parquet, ORC, and Avro file formats
- Experience with batch and streaming data pipelines, ETL/ELT frameworks, and orchestration tools
- Experience using Snowflake SnowConvert AI to migrate legacy SQL, stored procedures, ETL scripts, and database objects
- Familiarity with Bronze, Silver, and Gold medallion data architectures
- Experience with Snowflake Procedures, UDFs, Streams, Tasks, Dynamic Tables, and Snowpipe
- Hands-on Snowflake performance optimization using clustering keys, materialized views, query profiling, resource monitors, and warehouse sizing
- Working knowledge of Snowflake RBAC, data masking, row access policies, and tagging
- Experience with Secure Data Sharing, Snowflake Marketplace, and cross-region replication
- Familiarity with Snowflake Cortex and Snowpark for native data pipelines
- Knowledge of data warehousing, dimensional modeling, and slowly changing dimensions
- Strong SDLC practices, including Git, branching strategies, code reviews, and release management
- Experience with monitoring, logging, alerting, and observability frameworks for data pipelines
- Strong troubleshooting, debugging, and production support capabilities
- Extensive use of AI and LLM tools to accelerate data engineering work
- Excellent communication, problem-solving, and stakeholder management skills
- Snowflake SnowPro Core Certification
- Experience with Snowflake capabilities and dbt for data transformation
- SQL experience across legacy platforms such as Hive or Impala
- Experience with Snowpark or Snowflake migration tooling
- Experience with migration accelerators and remediation workflows
- Experience with data validation, reconciliation, and defect triage across source and target systems
- Experience with AWS S3, Glue, Redshift, EMR, Lambda, and Snowflake integration
- Experience with CI/CD pipelines such as Jenkins, GitHub Actions, or GitLab CI
- Terraform or Infrastructure as Code experience
- Familiarity with Kafka, Iceberg, Delta Lake, or modern streaming and lakehouse technologies
- Understanding of data governance, data cataloging, and metadata management
- Experience with hybrid or multi-cloud data pipelines and orchestration
Quantiphi Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Quantiphi and has not been reviewed or approved by Quantiphi.
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Wellbeing & Lifestyle Benefits — Wellbeing initiatives such as monthly meeting-free AMA-Zen Days, health check-ups, and wellness counseling are designed to reduce burnout and support day-to-day balance. Broader wellness programs reinforce both physical and mental health.
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Flexible Benefits — Remote/hybrid options with flexible working hours provide meaningful autonomy over where and when work gets done. Flexible leave constructs, including sabbaticals and special day leaves, add practical adaptability to the package.
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Parental & Family Support — Paid parental leave in the U.S., alongside maternity and childcare support, signals solid backing for families. These family-oriented policies integrate with a wider health and wellness focus.
Quantiphi Insights
What We Do
Quantiphi is an award-winning AI-first digital engineering company driven by the desire to solve transformational problems at the heart of business. Quantiphi solves the toughest and complex business problems by combining deep industry experience, disciplined cloud, and data-engineering practices, and cutting-edge artificial intelligence research to achieve quantifiable business impact at unprecedented speed.
