Job Title: Snowflake Data Engineer
Location: Remote
Job Type: Contract
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.
Key Responsibilities
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.
Required Skills & Experience
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
Functional / Domain Experience
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
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