Platform Engineering Lead/Senior Associate Director, Data and Analytics Specialist

Some careers shine brighter than others.

If you’re looking for a career that will help you stand out, join HSBC and fulfil your potential. Whether you want a career that could take you to the top, or simply take you in an exciting new direction, HSBC offers opportunities, support and rewards that will take you further. 

HSBC is one of the largest banking and financial services organisations in the world, with operations in 64 countries and territories. We aim to be where the growth is, enabling businesses to thrive and economies to prosper, and, ultimately, helping people to fulfil their hopes and realise their ambitions. 

We are currently seeking an experienced professional to join our team in the role of Senior Associate Director, Data and Analytics Specialist 

In this role, you will:

• Define the platform vision, roadmap, principles, and standards for Tier‑1 data services and establish and enforce federated governance across entitlement, privacy, retention, lineage, catalog, quality, integrity and usage monitoring.
• Define KPIs and operational measures (e.g., data freshness, quality scores, consumption latency, unit cost), and drive continuous improvement.
• Architect and evolve a scalable, secure, resilient, and cost‑optimised GCP data platform using services including: BigQuery, Dataflow, Pub/Sub, GKE, Cloud Storage, Bigtable, Composer/Airflow, IAM, Logging/Monitoring and Artifact Registry.
• Lead build-out of the Control Plane and unified portal (iHUB), enabling self‑service consumption and consistent multi‑tenant patterns, oversee data modelling and trusted source curation, including reference/master data strategy.
• Drive observability and reliability (metrics, tracing, anomaly detection, alerting) including AI‑assisted data issue identification/repair where appropriate.
• Own and mature CI/CD standards for data platform services and pipelines (e.g., GitLab CI/Jenkins), including automated testing, security scanning, and gated releases, implement Infrastructure as Code (Terraform) and GitOps-style operating practices for repeatable, auditable environment provisioning, embed security by design across the DevOps toolchain (IAM, KMS, secrets management, policy-as-code, vulnerability management).
• Define and establish SRE-aligned practices: SLO/SLI definition, error budgets, incident management, root-cause analysis, problem management, and reliability engineering, and strengthen operational controls: release governance, change management readiness, resilience/DR testing, runbooks, and on-call support patterns.
• Hands-on experience with agentic AI patterns (e.g., tool-using agents, planner–executor loops, retrieval-augmented generation (RAG), function/tool calling, and multi-agent collaboration) applied to engineering or data operations use cases (e.g., data quality triage, lineage inference, incident support, automated remediation).
• Exhibit experience applying FinOps practices to large-scale cloud data platforms, balancing cost, performance, reliability and risk in a regulated environment.Establish capability in cost modelling and unit economics for data platforms (e.g., cost per TB processed, cost per query/job, cost per pipeline run, cost per domain/tenant), with clear chargeback/showback approaches where applicable.
• Establish capability in cost modelling and unit economics for data platforms (e.g., cost per TB processed, cost per query/job, cost per pipeline run, cost per domain/tenant), with clear chargeback/showback approaches where applicable.

To be successful, you will:

• 15+ years data/platform engineering; 5+ years leading cloud data programs.
• Proven large‑scale GCP data architecture delivery.
• Strong SQL, Python, Terraform, streaming, governance implementation.
• Track record in building self‑service and multi‑tenant data platforms.
• Banking decisioning integration exposure (e.g., CDH/decisioning uplift) and/or CDMS integration.
• Practical experience delivering AI-enabled data platforms on cloud, including governed integration of GenAI/ML workloads with enterprise data services (catalogue, lineage, access controls, retention).
• Experience building and tracking FinOps KPIs and reporting (e.g., budget adherence, forecast accuracy, savings delivered, unit-cost trends), and driving continuous improvement with engineering teams
• Strong understanding of AI governance and controls in a regulated environment: model risk considerations, prompt/tooling guardrails, data leakage prevention, policy-based access, logging, monitoring, evaluation, and human-in-the-loop approval where required.

You’ll achieve more when you join HSBC.

www.hsbc.com/careers

HSBC is an equal opportunity employer committed to building a culture where all employees are valued, respected and opinions count. We take pride in providing a workplace that fosters continuous professional development, flexible working and, opportunities to grow within an inclusive and diverse environment. We encourage applications from all suitably qualified persons irrespective of, but not limited to, their gender or genetic information, sexual orientation, ethnicity, religion, social status, medical care leave requirements, political affiliation, people with disabilities, color, national origin, veteran status, etc., We consider all applications based on merit and suitability to the role.”

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Issued by – HSBC Software Development India