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. Lead Data Engineer will serve as the senior-most technical authority for enterprise data engineering services. The role is responsible for leading technical strategy, operational excellence, pipeline reliability, data quality, platform governance, and secure delivery of data services across ingestion, transformation, validation, publishing and recovery processes. The role will provide leadership across Snowflake, dbt, NiFi/OpenFlow, Dataiku, Power BI, SQL, Python, CI/CD for data engineering code, data quality controls, semantic models, BI refresh operations, RBAC, masking, encryption, audit trails, backup validation and DR readiness.

• Lead enterprise data engineering services across ingestion, transformation, validation, delivery, recovery, and pipeline reliability.
• Manage data pipelines across file, API, streaming, and batch ingestion patterns.
• Provide technical leadership for pipeline design reviews, operational readiness, troubleshooting, and recovery planning.
• Ensure data engineering operations are secure, auditable, SLA-driven, and governance-aligned.
• Operate, monitor, and optimize ingestion, transformation, and delivery pipelines across enterprise data platforms.
• Validate schema, metadata, format, structural integrity, lineage, and data quality before ingestion and downstream release.
• Manage retries, late-arriving data, partial loads, backfills, reruns, and out-of-order data handling.
• Define and govern data quality checks including nulls, referential integrity, completeness, consistency, timeliness, duplicates, gaps, and reconciliation.
• Enforce automated validation controls and ensure invalid datasets are blocked pending approval.
• Address schema drift, malformed data, and structural anomalies through approved operational handling.
• Lead implementation and governance of transformation frameworks using dbt and NiFi/OpenFlow.
• Provide technical oversight on logical/physical data models, transformation documentation, modelling standards, and pipeline design.
• Support performance tuning and optimization of SQL and Python-based data workloads.
• Lead operational governance of Snowflake, including environment setup, security controls, data domain onboarding, data models, and runbooks.
• Support Dataiku-based analytics workflows, dataset validation, schema checks, model artefact validation, and enterprise platform integration.
• Provide technical leadership for MicroStrategy and Power BI semantic models, dashboards, BI refresh cycles, publishing jobs, and platform stability.
• Maintain CI/CD pipelines, version control, traceability, release sequencing, testing, deployment, rollback, and change governance.
• Monitor throughput, connector health, validation rules, execution latency, failures, SLAs, dependencies, alerts, and platform availability.
• Provide technical RCA, operational reporting, audit evidence, log integrity, retention, backup validation, DR testing, and recovery support.
• Maintain architecture diagrams, lineage, runbooks, SOPs, dependency matrices, job definitions, KT material, and reusable knowledge artefacts.

• 8 to 10+ years of experience in data engineering with strong depth in enterprise data engineering, data platforms, data warehousing, BI operations and managed services.
• Prior experience in banking or financial services data platforms is strongly preferred.
• Proven experience leading Architects, senior data engineers, platform engineers, data analysts, BI engineers and technical SMEs.
• Strong experience operating and governing enterprise-grade data pipelines, data quality controls, transformation frameworks and analytical platforms
• Experience in core banking transformation, digital banking, data migration, reporting modernization, or regulatory change programs.
• Experience supporting production data pipelines, batch operations, regulatory submissions, and BI reporting solutions.
• Prior experience in banking, lending, risk, tax, securities, customer data, or regulatory reporting platforms.
• Exposure to European banking regulatory environments and compliance frameworks.
• Act as the final technical escalation point for complex data engineering, pipeline, platform, data quality and BI operational issues.
• Provide technical direction to data engineers, platform engineers, BI engineers, analysts and technical SMEs.
• Drive standardisation, automation, reliability and operational maturity across data engineering services.
• Ensure solutions are secure, compliant, auditable, scalable, resilient and operationally supportable.
• Collaborate with customer stakeholders, platform owners, security, compliance, business teams and third-party vendors.
• Mandatory Skills- SQL | Oracle | Python | Spark/PySpark | BODS | ODI | Snowflake | dbt |NiFi / OpenFlow | Dataiku | HOPEX |MicroStrategy | Power BI

• Bachelor's or Master's degree in Computer Science, Engineering, Information Technology, Data Engineering, or related field.
• Prior experience in banking, financial services, risk, finance, payments, lending, or regulatory reporting environments.
• Experience in regulated environments with focus on auditability, access controls, data governance, operational resilience and compliance.
• Demonstrated ability to influence architecture decisions, guide engineering teams and drive operational excellence across complex data ecosystems.