Role purpose :

The Senior Database/Data Platform Engineer will strengthen the central DB and middleware platform that supports enterprise data lake, analytics, CRM, and AI-first use cases across wealth management and related business platforms. The role is expected to build and scale reliable database foundations, high-performance data pipelines, and governed serving layers that can power reporting, operational applications, and AI/GenAI workloads.

Role summary :

This role sits within the central DB and middleware team and is responsible for designing, developing, optimizing, and operating data platforms across transactional and analytical systems. The engineer will work closely with architects, platform engineers, ETL teams, middleware teams, DevOps, and AI engineers to ensure that data is secure, performant, reusable, and production-ready.

Key objectives :

- Build and scale the enterprise data lake and central database layer for structured and semi-structured data.

- Improve database performance, availability, resiliency, and observability across business-critical workloads.

- Design and implement ETL/ELT pipelines for ingestion, transformation, reconciliation, and downstream serving.

- Create reusable data models and data products for analytics, CRM workflows, dashboarding, and AI use cases.

- Enable AI-first data access patterns such as governed query layers, semantic models, feature-ready datasets, and secure data-serving APIs.

- Reduce operational risk through automation of deployments, monitoring, backup, recovery, and housekeeping.

Key responsibilities :

- Design and maintain data models for transactional, reporting, and analytical workloads.

- Develop and optimize SQL for PostgreSQL, SQL Server, ClickHouse, and other relevant platforms.

- Build and maintain ETL/ELT workflows using tools such as Airflow, Talend, SSIS, or equivalent orchestration frameworks.

- Own database performance tuning, indexing, partitioning, query optimization, and storage strategy.

- Support database migration, schema evolution, and data quality validation across source and target systems.

- Implement and monitor backup, restore, disaster recovery, high availability, and production health checks.

- Collaborate with middleware and platform teams to expose governed and scalable data interfaces for internal platforms and AI services.

- Work with DevOps teams on CI/CD, infrastructure automation, observability, and environment management.

- Contribute to data governance through metadata standards, auditability, masking, access controls, and operational documentation.

- Support root cause analysis and production issue resolution for data platform incidents.

Required skills and experience :

- 6 to 12 years of experience in database engineering, data engineering, or analytics engineering.

- Strong hands-on experience in two or more of the following: PostgreSQL, MS SQL Server, ClickHouse, MongoDB, MySQL, Redis.

- Strong SQL skills with experience in query tuning, indexing, partitioning, and performance troubleshooting.

- Experience building ETL/ELT pipelines using Airflow, Talend, SSIS, Python, shell scripting, or similar tools.

- Experience in data modelling for both transactional and analytical systems.

- Hands-on understanding of data migration, reconciliation, and production support.

- Experience with cloud or platform environments such as AWS, Azure, Kubernetes, Linux, or containerized deployments.

- Familiarity with data observability, monitoring, logging, and alerting practices.

- Ability to work across multiple teams in a platform environment and convert business requirements into robust data solutions.

Preferred experience :

- Experience in BFSI, wealth management, lending, broking, insurance, or regulated industries.

- Experience with data lake, data warehouse, or lakehouse implementations.

- Experience with analytics stacks such as ClickHouse, Redshift, Elasticsearch, or similar serving layers.

- Exposure to Kafka, event-driven pipelines, or CDC-based ingestion.

- Understanding of AI/GenAI enablement patterns such as semantic data layers, text-to-SQL, metadata-driven access, feature datasets, and secure data exposure for LLM applications.

- Exposure to governance controls such as masking, RBAC, audit trails, and compliance-sensitive data handling.

Success measures :

- Improved query performance, lower latency, and better system throughput on critical workloads.

- Reduction in pipeline failures, manual intervention, and production incidents.

- Faster onboarding of new source systems and faster delivery of curated datasets.

- Higher reuse of central data assets across CRM, analytics, dashboards, and AI initiatives.

- Improved platform reliability through automation, monitoring, and recovery readiness.