Lakehouse implementation Engineer

Own the end-to-end architecture and technical roadmap for the Lakehouse platform, including data products, data marketplace, knowledge layer, and agentic workloads.

Define target architecture, reusable frameworks, scalability, security, performance, and operationalisation patterns for RAG, unstructured data, real-time and agentic workloads.

Partner with business and technology teams on data contracts, SLAs, data quality, and solution delivery.

Ensure delivery meets Bank software engineering, quality, and governance standards.

Lead technology evaluation through RFPs/POCs, software integration, and solution design reviews.

Drive performance optimisation, continuous improvement, and technical documentation.

Requirements

• 10–15 years’ experience implementing Data Lakehouse platforms , preferably in Financial Services, using Databricks, Snowflake, Cloudera, AWS, Azure, GCP, or similar.
• Strong experience with large-scale distributed data platforms and performance optimisation , including Iceberg/Hudi/Delta Lake, object/tiered storage, Trino/Denodo/Dremio, and distributed query engines.
• Experience designing MPP/distributed workloads across on-premise, hybrid, and cloud environments.
• Expertise in RAG and agentic workloads, including embeddings, Vector DB, Graph DB, prompt engineering, and context management.
• Strong knowledge of hybrid/cloud architecture, private connectivity, workload placement, egress optimisation, and Infrastructure-as-Code.
• Experience building and serving data products through APIs, pub/sub, real-time dashboards, generative BI, and data marketplaces.
• Experience with DevOps/engineering tools such as Jenkins, JIRA, Git, CI/CD pipelines, SonarQube, Terraform, monitoring, Control-M/Airflow, and testing tools.