Snowflake Architect – ADF-to-Snowflake Migration, Data Modeling & ELT/ETL
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
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10–13 years of overall experience in data engineering, data architecture, or related roles.
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At least 4–5 years of hands-on Snowflake architecture and administration experience.
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Proven hands-on experience migrating pipelines and workloads from Azure Data Factory (ADF) to Snowflake.
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Experience re-engineering ADF-based ELT/ETL logic into Snowflake-native pipelines using:
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Snowpipe
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Streams & Tasks
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dbt
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Or comparable Snowflake-compatible approaches
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Strong command of Snowflake features, including:
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Snowpipe
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Streams & Tasks
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Time Travel
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Zero-Copy Cloning
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Secure Data Sharing
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Virtual warehouse cost management
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Deep expertise in dimensional modeling, data vault, and enterprise data warehouse design principles.
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Strong SQL skills for data transformation, analysis, optimization, and troubleshooting.
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Hands-on experience with at least one ELT/ETL tool, including Informatica, dbt, Matillion, Talend, or ADF.
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Working knowledge of at least one cloud platform: AWS, Azure, or GCP.
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Understanding of cloud-native data services and data platform integration patterns.
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Experience with Python or another scripting language for automation and pipeline orchestration.
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Experience leading data platform migrations at enterprise scale.
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Ability to translate business requirements into technical architecture, data models, migration plans, and implementation guidance.
Preferred
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Experience in manufacturing, engineering, or BFSI environments.
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Experience designing enterprise-scale Snowflake migration strategies, landing zones, operating models, and governance frameworks.
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Familiarity with data quality, data lineage, metadata management, data cataloging, and data observability.
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Experience with real-time, near-real-time, and batch data-processing architectures.
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Exposure to cloud-native orchestration, serverless data services, event-driven pipelines, and automated deployment.
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Familiarity with Infrastructure as Code and CI/CD practices for data platforms.
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Experience optimizing Snowflake warehouses, workload management, storage, query performance, and consumption costs.
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Relevant Snowflake, cloud, data architecture, data engineering, or enterprise architecture certifications.
Overall Responsibilities
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Define Snowflake architecture strategies, target-state designs, reference architectures, technical standards, and implementation roadmaps.
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Lead the migration of pipelines and workloads from Azure Data Factory to Snowflake.
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Analyze existing ADF-based ELT/ETL processes and re-engineer them into Snowflake-native pipelines using Snowpipe, Streams & Tasks, dbt, or suitable alternatives.
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Design scalable, secure, reliable, and cost-efficient Snowflake data platforms.
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Apply Snowflake capabilities such as Time Travel, Zero-Copy Cloning, Secure Data Sharing, Snowpipe, Streams & Tasks, and warehouse cost management.
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Design and govern dimensional models, data vault structures, enterprise data warehouses, data marts, and related data platforms.
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Develop and review complex SQL for transformation, validation, data quality, reconciliation, and performance optimization.
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Design and implement batch, incremental, streaming, and event-driven data-processing patterns where required.
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Establish data platform standards for ingestion, transformation, storage, consumption, security, monitoring, recovery, and operational support.
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Use Python or another scripting language to automate pipeline orchestration, validation, monitoring, deployment, and operational processes.
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Collaborate with data engineers, application teams, cloud teams, security teams, business stakeholders, and delivery teams.
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Translate business requirements into data architecture, logical and physical data models, migration designs, and technical delivery plans.
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Lead technical discussions, design reviews, architecture decisions, code reviews, and migration planning sessions.
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Identify migration dependencies, technical risks, data-quality issues, performance constraints, and operational impacts.
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Support testing, validation, reconciliation, cutover, rollback planning, production stabilization, and post-migration optimization.
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Manage Snowflake compute and storage usage to improve performance while supporting responsible and sustainable resource consumption.
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Provide technical leadership for enterprise-scale data platform migration initiatives and ensure delivery against agreed quality, timeline, security, and cost objectives.
Strategic Objectives
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Modernize ADF-based data pipelines and workloads through Snowflake-native architecture.
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Establish scalable and governed data platforms that support analytics, reporting, integration, and business decision-making.
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Improve data quality, reliability, lineage, accessibility, and processing efficiency.
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Reduce unnecessary platform complexity and improve operational support through standardization and automation.
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Optimize Snowflake resource consumption, warehouse utilization, query performance, and overall platform cost.
Performance Outcomes
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ADF pipelines and workloads are migrated to Snowflake with validated functionality, data integrity, and agreed business continuity controls.
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Snowflake-native pipelines meet defined requirements for scalability, reliability, performance, security, and maintainability.
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Data models and warehouse designs support consistent, accurate, and reusable data consumption.
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Snowflake compute and storage resources are monitored and managed according to workload needs and cost objectives.
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Technical architecture, migration plans, data models, decisions, risks, and operational procedures are clearly documented.
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Enterprise stakeholders receive practical architecture recommendations aligned with business requirements and delivery constraints.
Technical Skills (By Category)
Programming Languages and Scripting
Essential
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Strong SQL skills for data transformation, querying, validation, reconciliation, troubleshooting, and optimization.
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Experience with Python or another scripting language for automation and pipeline orchestration.
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Ability to write scripts for data validation, pipeline control, monitoring, deployment, and operational support.
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Ability to interpret and re-engineer transformation logic from existing ELT/ETL workflows.
Preferred
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Experience developing reusable Python libraries or automation frameworks for data platforms.
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Familiarity with scripting standards, testing, version control, error handling, logging, and secure configuration.
Databases/Data Management
Essential
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Hands-on Snowflake architecture and administration experience of at least 4–5 years.
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Strong understanding of dimensional modeling, data vault, and enterprise data warehouse design principles.
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Experience designing fact tables, dimensions, relationships, keys, historization, data marts, and analytical structures.
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Experience with Snowflake Snowpipe, Streams & Tasks, Time Travel, Zero-Copy Cloning, Secu