Technical Delivery Manager(Data Lakehouse )

Key Responsibilities

1. Lead the design and implementation of scalable, secure, and regulatory-compliant enterprise data and analytics platforms for the banking/FSI domain.
2. Define data architecture strategies leveraging Lakehouse, Data Mesh, Data Fabric, and cloud-native data and AI technologies.
3. Architect and deliver data pipelines, APIs, data products, and data-serving layers for enterprise analytics.
4. Lead development of BI, reporting, predictive analytics, AI/ML, and real-time analytics solutions.
5. Drive AI/ML use cases including fraud detection, customer segmentation, credit scoring, risk modeling, and next-best-offer solutions.
6. Establish and implement DataOps, MLOps, DevOps, security, governance, and engineering standards.
7. Lead cross-functional teams of data engineers, BI developers, data scientists, and ML engineers.
8. Partner with business, architecture, application, testing, and vendor teams to define requirements and deliver solutions successfully.
9. Manage technical delivery, dependencies, testing, implementation, production cutover, and post-production support.
10. Provide technical leadership, resolve complex design issues, manage risks, and communicate delivery status to senior stakeholders.

Key Requirements

1. 10–15+ years of experience in data and analytics technology, with strong experience in banking/financial services.
2. Proven experience designing and implementing enterprise Data Lakehouse platforms using Databricks, Snowflake, Cloudera, AWS, Azure, or GCP.
3. Strong expertise in data engineering, data architecture, data modeling, integration, APIs, and enterprise analytics platforms.
4. Hands-on experience with technologies such as Spark, Python, SQL, Kafka, Airflow, Kubernetes, and CI/CD tools.
5. Strong knowledge of BI and analytics platforms such as SAS Viya, Teradata, Power BI, Qlik, Adobe, or equivalent technologies.
6. Experience implementing AI/ML solutions using Python, R, TensorFlow, PyTorch, MLflow, or similar technologies.
7. Strong understanding of banking domains including risk, fraud, credit, compliance, customer analytics, and regulatory requirements.
8. Proven experience leading multidisciplinary technology teams and managing complex enterprise technology deliveries.
9. Excellent understanding of Agile SDLC, DevOps/MLOps, data governance, security, model governance, explainability, and audit readiness.
10. Bachelor’s degree in Computer Science, Engineering, or equivalent; TOGAF, DAMA/DMBOK, CDMP, Microsoft Data & AI, SAS, or equivalent certifications are preferred.