Lead - Data Engineer

Who We Are

At Kyndryl, we run and reimagine the mission-critical technology systems that drive advantage for the world's leading businesses. We are at the heart of progress; with proven expertise and a continuous flow of AI-powered insight, enabling smarter decisions, faster innovation, and a lasting competitive edge. For our people-Kyndryls-that means doing purposeful work that powers human progress. Join us and experience a flexible, supportive environment where your well-being is prioritized and your potential can thrive.

The Role

Are you passionate about solving complex problems? Do you thrive in a fast-paced environment? Then there's a good chance you will love being a part of our Software Engineering - Development team at Kyndryl, where you will be able to see the immediate value of your work. The Data Engineer will be responsible for designing, building, optimizing, and maintaining scalable data infrastructure and data pipelines across hybrid on-premise and cloud-based data platforms. This role will support efficient data collection, ingestion, transformation, storage, processing, reporting, and analytics across banking domains including payments, finance, lending, investments, treasury, risk, and regulatory reporting. The candidate will ensure the stability, security, performance, and compliance of data platforms while enabling advanced analytics, BI reporting, and machine learning initiatives.

• Design, develop, and maintain scalable data pipelines, ETL/ELT workflows, data models, and data integration frameworks.
• Build and support data infrastructure across on-premise, cloud, and hybrid data platforms.
• Work with large banking datasets across payments, lending, finance, accounting, risk, treasury, investments, and regulatory reporting domains.
• Develop optimized SQL queries, stored procedures, data transformation logic, and reusable data engineering components.
• Use Python and/or Scala / Java for data processing, automation, validation, and platform integration.
• Support data ingestion, transformation, cleansing, enrichment, and loading into data warehouses, data lakes, and lakehouse platforms.
• Work with tools and platforms such as MicroStrategy, RMT, Python, Dataiku, and other analytics/reporting platforms.
• Develop and maintain data workflows using ETL/ELT frameworks and orchestration tools such as Airflow or similar scheduling tools.
• Support big data processing using technologies such as Spark and Kafka where applicable.
• Collaborate with data analysts, data scientists, architects, platform engineers, risk teams, finance teams, and business stakeholders.
• Ensure data quality, data reconciliation, metadata management, data lineage, and auditability across data pipelines.
• Implement controls for data security, privacy, access management, and compliance with GDPR and European banking regulations.
• Support CI/CD, version control, deployment automation, and infrastructure-as-code practices for data engineering solutions.
• Monitor, troubleshoot, and optimize performance of data pipelines, jobs, databases, and cloud data platforms.
• Support advanced analytics, reporting, dashboarding, and machine learning use cases by providing reliable and trusted data assets.

• 5 to 7+ years of experience in data engineering, data platform engineering, ETL/ELT development, or data warehouse engineering.
• Prior experience in banking or financial services data platforms is strongly preferred.
• Design, develop, and maintain scalable data pipelines, ETL/ELT workflows, data models, and data integration frameworks.
• Experience with Dataiku workflows for data preparation, transformation, pipeline automation, lineage, data quality validation, and integration with platforms such as Snowflake, MicroStrategy, Power BI and cloud-based data ecosystems.
• Strong programming skills in Python; exposure to Scala or Java is preferred.
• Advanced SQL skills for querying, transformation, optimization, reconciliation, and analysis of large datasets.
• Hands-on experience with ETL / ELT frameworks and data integration patterns.
• Experience with data warehouses, data lakes, and lakehouse architectures.
• Knowledge and experience of MicroStrategy, RMT, Python, and Dataiku.
• Experience with big data technologies such as Apache Spark and Kafka is preferred.
• Experience with workflow orchestration tools such as Airflow or similar tools.
• Ability to build reusable, scalable, and production-ready data pipelines.
• Strong understanding of data modelling, data partitioning, performance tuning, and data pipeline optimization.
• Experience in data validation, data quality checks, reconciliation, and root-cause analysis.
• Knowledge of data governance, metadata management, data cataloguing, and data lineage.
• Experience implementing data quality checks, validation rules, reconciliation controls, and monitoring frameworks.
• Understanding of GDPR and European data privacy regulations.
• Awareness of audit, regulatory, operational risk, and security requirements in banking.
• Ability to implement secure data access, encryption, masking, and role-based access controls where required.

• Bachelor's or Master's degree in Computer Science, Engineering, Information Technology, Data Engineering, or related field.
• Tools : SQL | Oracle | Python | Spark/PySpark | BODS | ODI | Snowflake | dbt |NiFi / OpenFlow | Dataiku | HOPEX |MicroStrategy | Power BI
• Prior experience in banking, financial services, risk, finance, payments, lending, or regulatory reporting environments.
• Experience supporting advanced analytics, BI reporting, and machine learning data pipelines.
• Exposure to Dataiku-based analytics workflows and integration with enterprise data platforms.
• Experience with MicroStrategy reporting ecosystems and downstream reporting consumption patterns.
• Knowledge of regulatory data requirements and audit-driven reporting processes.
• Experience working in Agile, Scrum, DevOps, or Waterfall delivery models.