4Bell Technology

Staffing & Recruiting

Full Stack Data Engineer(R-1513)

3,000,000.00-3,600,000.00/A

Any Degree

IT (Information Technology)

Full-time

Mumbai

22-Oct-2026

Data Engineer AWS Data modelling Python Snowflake Advance SQL Data warehouse/Data lake DBT APIs and data integrations Kafka & Docker

Job Description

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·         Develop and maintain Snowflake-based data products and data models.

·         Build and manage ELT pipelines using dbt and modern data engineering practices.

·         Implement automated data quality checks, monitoring and observability across data pipelines.

·         Deliver reliable, secure and well-governed datasets for analytics and operational use cases.

·         Collaborate with Product Managers, Analysts and Business Stakeholders to understand data requirements and deliver fit-for-purpose solutions.

·         Design solutions with scalability, maintainability, performance and cost optimisation in mind.

·         Support real-time and near-real-time data integration patterns using Kafka and event-driven architectures.

·         Develop reusable frameworks, standards and engineering best practices across the data platform.

·         Implement CI/CD pipelines and automated deployment processes.

·         Troubleshoot and resolve data quality, performance and reliability issues.

·         Contribute to platform modernisation and continuous improvement initiatives.

·         Work closely with Data Governance teams to ensure compliance with GDPR, security and data management standards.

·         Participate in code reviews and promote engineering excellence across the team.

·         Mentor junior engineers and contribute to knowledge sharing across the Data & Analytics community.

·         Evaluate emerging technologies and recommend improvements to the platform and engineering practices.

·         Support AI and advanced analytics initiatives through the delivery of trusted, high-quality data assets


Data Engineering

  • Advanced SQL
  • Python
  • Data Modelling (Dimensional Modelling, Kimball, Data Vault fundamentals)
  • ELT/ETL design and development
  • Data Warehousing concepts
  • Data Lakehouse concepts
  • Data Quality Engineering
  • Snowflake
  • dbt
  • Git
  • CI/CD pipelines
  • Data Observability and Monitoring
  • AWS fundamentals
  • Infrastructure as Code (Terraform)
  • APIs and data integrations
  • Containerisation fundamentals (Docker)
  • Kafka fundamentals
  • Event-Driven Architecture concepts
  • CDC (Change Data Capture) concepts
  • Agile delivery (Scrum/Kanban)
  • Test-driven engineering principles
  • Code review practices
  • Performance optimisation
  • Cost optimisation