Data Engineer

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 GCP 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