Sobre este puesto de Snowflake Architect – ADF-to-Snowflake Migration, Data Modeling & ELT/ETL en Synechron
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 stabilization, and post-migration optimization.
Manage Snowflake compute and storage usage to improve performance while supporting responsible and sustainable resource consumption.
Provide technical leadership for enterprise-scale data platform migration initiatives and ensure delivery against agreed quality, timeline, security, and cost objectives.
Strategic Objectives
Modernize ADF-based data pipelines and workloads through Snowflake-native architecture.
Establish scalable and governed data platforms that support analytics, reporting, integration, and business decision-making.
Improve data quality, reliability, lineage, accessibility, and processing efficiency.
Reduce unnecessary platform complexity and improve operational support through standardization and automation.
Optimize Snowflake resource consumption, warehouse utilization, query performance, and overall platform cost.
Performance Outcomes
ADF pipelines and workloads are migrated to Snowflake with validated functionality, data integrity, and agreed business continuity controls.
Snowflake-native pipelines meet defined requirements for scalability, reliability, performance, security, and maintainability.
Data models and warehouse designs support consistent, accurate, and reusable data consumption.
Snowflake compute and storage resources are monitored and managed according to workload needs and cost objectives.
Technical architecture, migration plans, data models, decisions, risks, and operational procedures are clearly documented.
Enterprise stakeholders receive practical architecture recommendations aligned with business requirements and delivery constraints.
Technical Skills (By Category)
Programming Languages and Scripting
Essential
Strong SQL skills for data transformation, querying, validation, reconciliation, troubleshooting, and optimization.
Experience with Python or another scripting language for automation and pipeline orchestration.
Ability to write scripts for data validation, pipeline control, monitoring, deployment, and operational support.
Ability to interpret and re-engineer transformation logic from existing ELT/ETL workflows.
Preferred
Experience developing reusable Python libraries or automation frameworks for data platforms.
Familiarity with scripting standards, testing, version control, error handling, logging, and secure configuration.
Databases/Data Management
Essential
Hands-on Snowflake architecture and administration experience of at least 4–5 years.
Strong understanding of dimensional modeling, data vault, and enterprise data warehouse design principles.
Experience designing fact tables, dimensions, relationships, keys, historization, data marts, and analytical structures.
Experience with Snowflake Snowpipe, Streams & Tasks, Time Travel, Zero-Copy Cloning, Secure Data Sharing, and warehouse cost management.
Experience migrating ADF-based ELT/ETL logic into Snowflake-native pipelines.
Understanding of data ingestion, transformation, incremental processing, data quality, reconciliation, lineage, and lifecycle management.
Ability to design data platforms for batch, real-time, and near-real-time processing.
Preferred
Experience with data lakes, lakehouses, data marts, semantic layers, and enterprise reporting platforms.
Familiarity with data governance, metadata management, data cataloging, data classification, and data observability.
Experience with data retention, archiving, recovery, and storage optimization.
Cloud Technologies
Essential
Working knowledge of at least one cloud platform: AWS, Azure, or GCP.
Understanding of cloud-native data services and their integration with Snowflake.
Experience designing secure and scalable cloud-based data architectures.
Understanding of cloud networking, identity and access management, storage, monitoring, availability, and resource management.
Preferred
Experience integrating Snowflake with cloud storage, messaging, orchestration, monitoring, and serverless services.
Familiarity with hybrid and multi-cloud data architectures.
Experience optimizing cloud data services for performance, cost, and sustainable resource consumption.
Frameworks and Libraries
Essential
Hands-on experience with at least one ELT/ETL tool, including Informatica, dbt, Matillion, Talend, or ADF.
Experience designing and implementing Snowflake-native ingestion and transformation frameworks.
Understanding of pipeline orchestration, dependency management, error handling, retry logic, data validation, and operational recovery.
Experience applying reusable patterns for ingestion, transformation, incremental loading, and data reconciliation.
Preferred
Experience with dbt models, tests, documentation, macros, and deployment practices.
Familiarity with event-driven and streaming data frameworks.
Exposure to data-quality, observability, lineage, and pipeline-monitoring frameworks.
Development Tools and Methodologies
Essential
Experience leading enterprise-scale data platform migration initiatives.
Experience with architecture documentation, technical design reviews, migration planning, and implementation governance.
Understanding of software development lifecycle practices for data platforms.
Ability to define technical standards, reference patterns, operational procedures, and support models.
Experience with testing, data reconciliation, performance testing, cutover planning, rollback planning, and production stabilization.
Ability to collaborate with business, data, cloud, security, application, and infrastructure teams.
Preferred
Experience with Git, CI/CD pipelines, Infrastructure as Code, and automated data-platform deployment.
Familiarity with Agile, iterative, or structured delivery methodologies.
Experience with automated data-quality checks, deployment validation, and platform compliance controls.
Exposure to architecture review processes and enterprise data governance forums.
Security Protocols
Essential
Understanding of Snowflake security, including role-based access control, least-privilege access, secure data sharing, authentication, and authorization.
Experience defining access controls for databases, schemas, tables, views, warehouses, pipelines, and data-sharing capabilities.
Awareness of data protection, encryption, masking, privacy, auditability, retention, and compliance requirements.
Ability to ensure data migration and platform operations follow approved security, governance, and change-management controls.
Preferred
Experience with dynamic data masking, row-level access policies, network controls, key management, and sensitive-data handling.
Familiarity with cloud security controls and secure integration between Snowflake and cloud-native services.
Experience supporting regulated or controlled data environments, including BFSI data platforms.
Experience Requirements
10–13 years of overall data engineering or architecture experience.
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 to Snowflake.
Demonstrable experience re-engineering ADF-based ELT/ETL logic into Snowflake-native pipelines using Snowpipe, Streams & Tasks, dbt, or equivalent approaches.
Strong experience with Snowflake architecture, administration, performance management, security, data sharing, and cost optimization.
Deep experience with dimensional modeling, data vault, and enterprise data warehouse design.
Strong SQL skills and hands-on experience with at least one ELT/ETL tool, including Informatica, dbt, Matillion, Talend, or ADF.
Working knowledge of AWS, Azure, or GCP and cloud-native data services.
Experience using Python or another scripting language for automation and pipeline orchestration.
Prior experience leading data platform migrations at enterprise scale.
Experience in manufacturing, engineering, or BFSI environments is preferred.
Candidates may qualify through equivalent hands-on data architecture and migration experience if they can demonstrate comparable Snowflake delivery, ADF migration, enterprise data modeling, platform governance, and stakeholder outcomes.
Day-to-Day Activities
Review current ADF pipelines, workloads, dependencies, data models, transformation logic, and operational issues, then define Snowflake-native migration and modernization approaches.
Collaborate with data engineers, cloud teams, application teams, security teams, business stakeholders, and delivery teams through design reviews, planning sessions, technical workshops, and migration checkpoints.
Produce target-state architectures, data models, migration plans, Snowflake pipeline designs, SQL transformations, cost assessments, validation results, technical decisions, and operational documentation.
Make architecture decisions within the assigned scope, resolve technical dependencies, guide implementation teams, manage migration risks, and escalate decisions requiring broader governance or stakeholder approval.
Qualifications
Bachelor's degree in Computer Science, Information Technology, Engineering, Data Engineering, or a related field; equivalent relevant professional experience may also be considered.
10–13 years of overall data engineering or architecture experience, including at least 4–5 years of hands-on Snowflake architecture and administration.
Relevant Snowflake, cloud, data architecture, data engineering, or enterprise architecture certifications are preferred.
Training in Snowflake architecture, ADF migration, data vault, dimensional modeling, ELT/ETL, cloud-native data services, SQL, Python, security, and cost management is beneficial.
Maintain continuous professional development in Snowflake capabilities, data architecture, cloud platforms, migration methods, data governance, automation, security, performance, and sustainable data-platform operations.
Professional Competencies
Apply structured analysis and problem-solving to assess migration complexity, pipeline dependencies, data quality, performance, security, and operational risks.
Provide technical leadership across data engineering, architecture, cloud, security, application, and delivery teams while supporting shared implementation outcomes.
Translate business requirements into clear technical architecture, data models, migration plans, platform standards, and implementation guidance.
Adapt architecture and migration approaches as business priorities, data requirements, technology capabilities, and delivery constraints change.
Identify opportunities to improve data-platform scalability, automation, reuse, reliability, performance, cost efficiency, and sustainable resource utilization.
Prioritize architecture decisions, migration activities, technical risks, stakeholder needs, and delivery commitments while maintaining clear documentation and follow-through.
SYNECHRON’S DIVERSITY & INCLUSION STATEMENT
Diversity & Inclusion are fundamental to our culture, and Synechron is proud to be an equal opportunity workplace and is an affirmative action employer. Our Diversity, Equity, and Inclusion (DEI) initiative ‘Same Difference’ is committed to fostering an inclusive culture – promoting equality, diversity and an environment that is respectful to all. We strongly believe that a diverse workforce helps build stronger, successful businesses as a global company. We encourage applicants from across diverse backgrounds, race, ethnicities, religion, age, marital status, gender, sexual orientations, or disabilities to apply. We empower our global workforce by offering flexible workplace arrangements, mentoring, internal mobility, learning and development programs, and more.
All employment decisions at Synechron are based on business needs, job requirements and individual qualifications, without regard to the applicant’s gender, gender identity, sexual orientation, race, ethnicity, disabled or veteran status, or any other characteristic protected by law.
