Snowflake Architect - ADF-to-Snowflake Migration, Data Modeling & ELT/ETL

Synechron; Mumbai; On-site; INR 4,200,000 - 5,400,000; Full time

Job summary

Synechron seeks a seasoned Snowflake Architect to drive enterprise-scale data platform architecture and modernization. You will lead migrations from Azure Data Factory to Snowflake, re-engineering ELT/ETL logic into Snowflake-native pipelines using Snowpipe, Streams & Tasks, and dbt.

The role emphasizes governance, performance, and cost efficiency across cloud data services. The ideal candidate has 10–13 years in data engineering or architecture with strong SQL skills, Python scripting, and

Qualifications

• 10-13 years of experience in data engineering, data architecture, or related roles.
• At least 4-5 years of hands-on Snowflake architecture and administration experience.
• Proven hands-on experience migrating pipelines and workloads from Azure Data Factory (ADF) to Snowflake.
• Experience re-engineering ADF-based ELT/ETL logic into Snowflake-native pipelines using Snowpipe, Streams & Tasks, dbt, or comparable approaches.

Responsibilities

• Define Snowflake architecture strategies, target-state designs, reference architectures, technical standards, and implementation roadmaps.
• Lead the migration of pipelines and workloads from Azure Data Factory to Snowflake.
• Analyze existing ADF-based ELT/ETL processes and re-engineer them into Snowflake-native pipelines using Snowpipe, Streams & Tasks, dbt, or suitable alternatives.
• Design scalable, secure, reliable, and cost-efficient Snowflake data platforms.
• Apply Snowflake capabilities such as Time Travel, Zero-Copy Cloning, Secure Data Sharing, Snowpipe, Streams & Tasks, and warehouse cost management.
• Design and govern dimensional models, data vault structures, enterprise data warehouses, data marts, and related data platforms.
• Develop and review complex SQL for transformation, validation, data quality, reconciliation, and performance optimization.
• Design and implement batch, incremental, streaming, and event-driven data-processing patterns where required.
• Establish data platform standards for ingestion, transformation, storage, consumption, security, monitoring, recovery, and operational support.
• Use Python or another scripting language to automate pipeline orchestration, validation, monitoring, deployment, and operational processes.
• Collaborate with data engineers, application teams, cloud teams, security teams, business stakeholders, and delivery teams.
• Translate business requirements into data architecture, logical and physical data models, migration designs, and technical delivery plans.
• Lead technical discussions, design reviews, architecture decisions, code reviews, and migration planning sessions.
• Identify migration dependencies, technical risks, data-quality issues, performance constraints, and operational impacts.
• Support testing, validation, reconciliation, cutover, rollback planning, production stabilizati

Skills

Snowflake architecture

Data platform modernization

ELT/ETL orchestration

SQL proficiency

Cloud platforms (AWS/Azure/GCP)

Python scripting

Migration planning

Stakeholder management

Tools

Snowpipe

Streams & Tasks

dbt

Informatica

Matillion

Talend

Azure Data Factory (ADF)

Job description

Job Summary

Synechron is seeking a Snowflake Architect with 10-13 years of overall data engineering or architecture experience, including at least 4-5 years of hands-on Snowflake architecture and administration.The role will lead enterprise-scale data platform architecture, modernization, and migration initiatives, with a specific focus on migrating pipelines and workloads from Azure Data Factory (ADF) to Snowflake. The successful candidate will re-engineer ADF-based ELT/ETL logic into Snowflake-native pipelines using Snowpipe, Streams & Tasks, dbt, or comparable approaches.This position contributes to business objectives by improving data platform scalability, reliability, governance, performance, cost efficiency, and accessibility. The role also requires strong stakeholder management and the ability to translate business requirements into practical technical architecture.

Software Requirements

Required

• 10-13 years of overall experience in data engineering, data architecture, or related roles.
• At least 4-5 years of hands-on Snowflake architecture and administration experience.
• Proven hands-on experience migrating pipelines and workloads from Azure Data Factory (ADF) to Snowflake.
• Experience re-engineering ADF-based ELT/ETL logic into Snowflake-native pipelines using:
  • Snowpipe
  • Streams & Tasks
  • dbt
  • Or comparable Snowflake-compatible approaches
• Strong command of Snowflake features, including:
  • Snowpipe
  • Streams & Tasks
  • Time Travel
  • Zero-Copy Cloning
  • Secure Data Sharing
  • Virtual warehouse cost management
• Deep expertise in dimensional modeling, data vault, and enterprise data warehouse design principles.
• Strong SQL skills for data transformation, analysis, optimization, and troubleshooting.
• Hands-on experience with at least one ELT/ETL tool, including Informatica, dbt, Matillion, Talend, or ADF.
• Working knowledge of at least one cloud platform: AWS, Azure, or GCP.
• Understanding of cloud-native data services and data platform integration patterns.
• Experience with Python or another scripting language for automation and pipeline orchestration.
• Experience leading data platform migrations at enterprise scale.
• Ability to translate business requirements into technical architecture, data models, migration plans, and implementation guidance.

Preferred

• Experience in manufacturing, engineering, or BFSI environments.
• Experience designing enterprise-scale Snowflake migration strategies, landing zones, operating models, and governance frameworks.
• Familiarity with data quality, data lineage, metadata management, data cataloging, and data observability.
• Experience with real-time, near-real-time, and batch data-processing architectures.
• Exposure to cloud-native orchestration, serverless data services, event-driven pipelines, and automated deployment.
• Familiarity with Infrastructure as Code and CI/CD practices for data platforms.
• Experience optimizing Snowflake warehouses, workload management, storage, query performance, and consumption costs.
• Relevant Snowflake, cloud, data architecture, data engineering, or enterprise architecture certifications.

Overall Responsibilities

• Define Snowflake architecture strategies, target-state designs, reference architectures, technical standards, and implementation roadmaps.
• Lead the migration of pipelines and workloads from Azure Data Factory to Snowflake.
• Analyze existing ADF-based ELT/ETL processes and re-engineer them into Snowflake-native pipelines using Snowpipe, Streams & Tasks, dbt, or suitable alternatives.
• Design scalable, secure, reliable, and cost-efficient Snowflake data platforms.
• Apply Snowflake capabilities such as Time Travel, Zero-Copy Cloning, Secure Data Sharing, Snowpipe, Streams & Tasks, and warehouse cost management.
• Design and govern dimensional models, data vault structures, enterprise data warehouses, data marts, and related data platforms.
• Develop and review complex SQL for transformation, validation, data quality, reconciliation, and performance optimization.
• Design and implement batch, incremental, streaming, and event-driven data-processing patterns where required.
• Establish data platform standards for ingestion, transformation, storage, consumption, security, monitoring, recovery, and operational support.
• Use Python or another scripting language to automate pipeline orchestration, validation, monitoring, deployment, and operational processes.
• Collaborate with data engineers, application teams, cloud teams, security teams, business stakeholders, and delivery teams.
• Translate business requirements into data architecture, logical and physical data models, migration designs, and technical delivery plans.
• Lead technical discussions, design reviews, architecture decisions, code reviews, and migration planning sessions.
• Identify migration dependencies, technical risks, data-quality issues, performance constraints, and operational impacts.
• Support testing, validation, reconciliation, cutover, rollback planning, production stabilizati