Snowflake Engineer with Azure

Role Summary

We are seeking skilled Data Engineers with strong expertise in Snowflake, data-pipeline development, orchestration, ETL/ELT, cloud data platforms, and modern data-engineering practices. The role will design, build, orchestrate, and optimize scalable pipelines and data products that support analytics, reporting, AI/ML, and operational needs.

The ideal candidate has hands-on experience with Snowflake, Azure Data Factory, and modern transformation and data-management tooling, including Coalesce Transform, Catalog, and Quality. The successful candidate will collaborate with Product, Analytics, Architecture, and Business teams to deliver reliable, secure, observable, and high-performing data solutions.

Pipeline Engineering and Orchestration

• Design, develop, and maintain scalable batch, near-real-time, and real-time data pipelines.
• Build reusable ingestion, transformation, orchestration, scheduling, and data-delivery components.
• Implement dependency management, retries, monitoring, alerting, logging, and operational recovery.
• Optimize pipelines for performance, reliability, maintainability, and cost.

Snowflake Development

• Build Snowflake databases, schemas, tables, views, Dynamic Tables, Tasks, Streams, Snowpipe processes, and stored procedures.
• Implement data models supporting reporting, analytics, semantic layers, and AI use cases.
• Apply query tuning, workload optimization, clustering, and cost-management practices.

Transformation and Data Management

• Develop modular transformation workflows using Coalesce Transform.
• Support metadata discovery, documentation, and lineage using Coalesce Catalog.
• Implement automated validation and quality controls using Coalesce Quality.
• Orchestrate ingestion and integration workflows using Azure Data Factory.

Integration and Modernization

• Integrate data from enterprise applications, APIs, databases, files, and third-party systems.
• Modernize legacy ETL processes for cloud-native Snowflake architectures.
• Support API-based and event-driven integration patterns where appropriate.

Data Quality, Governance, and Delivery

• Implement validation, reconciliation, exception handling, observability, lineage, and auditability.
• Follow enterprise security, privacy, and governance standards.
• Participate in Agile delivery, technical design, production support, and continuous improvement.

Target experience bands

• Data Engineer: 5–7 years of relevant data engineering, ETL/ELT, or data warehousing experience.
• Senior Data Engineer: 7–10 years of relevant experience, including ownership of complex pipelines and technical guidance.
• For both levels, hands-on Snowflake experience and experience in cloud-based analytical environments are required. Final alignment to CitiusTech designation and compensation bands should be confirmed through Talent Acquisition.

Mandatory Skills

• Snowflake architecture and development
• Snowflake performance optimization, security, Streams, Tasks, Dynamic Tables, Snowpipe, Time Travel, stored procedures, and functions
• Azure Data Factory for pipeline development and orchestration
• Coalesce Transform, Coalesce Catalog, and Coalesce Quality
• Advanced SQL and data modeling
• ETL/ELT design, data warehousing, data-lake, and lakehouse concepts
• Pipeline scheduling, dependency management, monitoring, alerting, and recovery
• Python and SQL; PySpark and shell scripting preferred
• Cloud experience with Azure; AWS or Google Cloud Platform exposure is beneficial
• Data quality, metadata, lineage, CI/CD, and DataOps practices

Experience in at least one of the following business or industry contexts

• Finance, Sales, or Operations analytics
• Healthcare
• EdTech or Learning Technology
• SaaS platforms
• Ability to understand business data needs and translate them into reliable, reusable data products

Technical Skills

• Snowflake Data Cloud and cloud-native data engineering
• Azure Data Factory pipelines, triggers, integration runtimes, monitoring, and deployment
• Coalesce transformation workflows, cataloging, lineage, and quality controls
• Advanced SQL, Python, data modeling, and performance tuning
• REST APIs and event-driven architectures
• CI/CD, automated testing, infrastructure automation, and DataOps
• AI/ML data-preparation pipelines and AI-ready datasets