Data Engineer

End Date

Friday 30 October 2026

We Support Flexible Working – Click here for more information on flexible working options

Flexible Working Options

Hybrid Working

Job Description Summary

The Data Engineer will focus on assisting in the design, development, and maintenance of Data Product pipelines, gaining practical experience whilst working under the guidance of senior data engineers.​

Job Description

Data Engineer – Feature Team / Delivery Squad

Location: India (Hybrid)
Department: Data Engineering
Experience: 4-8 Years

Role Summary

We are looking for a skilled Data Engineer to join Agile Feature Teams delivering data products, regulatory reporting solutions, modern data platforms, and analytics capabilities. The role involves designing, developing, and enhancing scalable data pipelines, integrating enterprise data sources, supporting cloud data platforms, and working closely with Product Owners, Architects, Analysts, and Business stakeholders to deliver high-quality data solutions.

Key Responsibilities

Data Engineering & Development

• Design, develop, and maintain scalable data pipelines and ETL/ELT solutions.
• Build data ingestion, transformation, and integration frameworks across multiple source systems.
• Develop reusable and high-performance data models, datasets, and APIs.
• Support modernization, migration, and cloud transformation initiatives.

Agile Delivery & Feature Development

• Work within Agile Scrum teams to deliver features aligned to business priorities.
• Participate in sprint planning, backlog refinement, estimation, and reviews.
• Collaborate with Product Owners and Business Analysts to translate requirements into technical solutions.
• Deliver features that meet functional, non-functional, and regulatory requirements.

Data Platform Engineering

• Build and optimize data warehouse, lakehouse, and reporting solutions.
• Develop solutions using Azure Data Services, Databricks, and modern cloud technologies.
• Ensure scalability, reliability, and performance of data platforms.
• Support data architecture standards and engineering best practices.

Data Quality & Governance

• Implement data quality checks, reconciliation controls, and validation frameworks.
• Ensure compliance with data governance, lineage, security, and regulatory requirements.
• Support audit readiness and control frameworks.

Testing & Release Support

• Develop unit, system, and integration testing components.
• Participate in release planning and deployment activities.
• Support defect resolution and production readiness activities.
• Collaborate with BAU teams during transition and hypercare phases.

Continuous Improvement

• Automate development, testing, and deployment processes.
• Improve engineering standards, reusable assets, and delivery efficiency.
• Contribute to platform modernization and innovation initiatives.

Required Skills

Data Engineering

• SQL Server, Oracle, PostgreSQL
• Snowflake, Databricks
• Data Warehousing & Data Modelling
• Data Lakehouse Architecture

ETL / Integration

• Azure Data Factory (ADF)
• SSIS / Informatica
• Apache Airflow
• API Integration
• Event-Driven Data Processing

Cloud Technologies

• Microsoft Azure
• Azure Synapse Analytics
• Azure Storage
• Azure Data Lake
• Azure DevOps

Programming

• SQL
• Python
• PySpark
• Spark
• Git Version Control

Data Governance

• Data Quality Frameworks
• Data Lineage
• Metadata Management
• Regulatory Reporting Controls

Experience & Qualifications

Essential

• 4-8 years of experience in Data Engineering or Data Platform Development.
• Strong SQL, Python, and ETL development expertise.
• Experience delivering enterprise data and analytics solutions.
• Hands-on experience with Azure Data Platform services.
• Strong problem-solving and stakeholder management skills.
• Experience working in Agile delivery teams.

Preferred

• Banking or Financial Services experience.
• Regulatory reporting or risk data experience.
• Databricks and Snowflake experience.
• CI/CD and DevOps practices.
• Knowledge of modern data architecture patterns.

Key Success Measures

• Sprint commitments delivered on time and to quality standards.
• Reliable and scalable data products and pipelines.
• Reduced technical debt and improved platform performance.
• Successful delivery of business and regulatory initiatives.
• Improved automation, reusability, and engineering productivity.
• Positive stakeholder and product owner feedback.