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.