Senior Data Engineer (Banking Domain)
Digifloat
Role:Role OverviewWe are seeking a highly skilled Senior Data Engineer with strong hands-on experience in the banking domain to design, develop, and maintain enterprise-scale data solutions. This role requires an engineer who has personally built business-centric data marts, Customer 360 solutions, analytical datasets, and end-to-end data pipelines rather than primarily focusing on cloud infrastructure or platform administration.
The ideal candidate will have extensive experience working with banking datasets, advanced SQL development, PySpark-based transformations, and modern data warehouse architectures while supporting reporting, analytics, and AI/ML initiatives.
Key ResponsibilitiesBanking Data Engineering
Design and develop Retail Banking, SME Banking, and Corporate Banking data marts.
Build Customer 360 solutions that provide unified customer views across banking products and channels.
Develop dimensional data models, fact tables, dimension tables, and reusable data products.
Translate complex business requirements into scalable data solutions.
Data Pipeline Development
Design and implement end-to-end data ingestion, transformation, orchestration, and delivery pipelines.
Develop scalable ETL/ELT solutions using Python, PySpark, and SQL.
Implement Change Data Capture (CDC) mechanisms and incremental loading strategies.
Handle inserts, updates, deletes, and historical data management efficiently.
SQL Development & Optimization
Develop complex SQL queries, stored procedures, and data processing logic.
Optimize large-scale data workloads and warehouse performance.
Utilize advanced SQL features including:
Common Table Expressions (CTEs)
Window Functions
Complex Joins
Query Tuning & Optimization
Partitioning Strategies
Banking Analytics & AI Enablement
Develop analytical datasets and feature-ready data assets for reporting and machine learning initiatives.
Support data preparation requirements for predictive analytics, AI/ML models, and customer intelligence use cases.
Collaborate with analytics and data science teams to enable advanced business insights.
Data Quality & Reconciliation
Ensure data accuracy, completeness, and consistency across enterprise systems.
Investigate and resolve historical data gaps and data quality issues.
Design reconciliation frameworks and data validation processes.
Apply estimation and remediation techniques when historical data is incomplete or unavailable.
Production Support & Governance
Monitor and support production data pipelines and data marts.
Troubleshoot data issues and performance bottlenecks.
Maintain documentation, lineage, and governance standards.
Participate in release planning, deployments, and production support activities.
Required Banking Domain ExperienceCandidates should possess hands-on experience in one or more of the following areas:
Customer Accounts
Deposits & Transactions
Payments & Transfers
Retail Banking
SME Banking
Corporate Banking
Loans & Financing
Credit Facilities
Trade Finance
Letters of Credit (LC)
Treasury or Financial Analytics
Preference will be given to candidates who have directly developed business-focused banking data solutions and can clearly demonstrate ownership of data mart and analytical solution development.
Required Qualifications
Bachelor's degree in Computer Science, Information Systems, Data Engineering, or related field.
7+ years of hands-on Data Engineering experience.
Proven experience delivering enterprise-scale data warehouse and analytics projects within banking or financial services.
Strong understanding of data modeling principles and banking data structures.
Demonstrated experience personally building data pipelines and business-facing data products.
Interested candidates may share their profiles to careers@digifloat.com
