Director - Data Platform Engineering

We are looking for an experienced and hands-on Director - Data Platform Engineering to lead the design, development, and evolution of a large-scale cloud-native data platform built on Google Cloud Platform (GCP). This role requires a strong engineering leader with deep expertise in distributed data systems, real-time streaming architectures, modern lakehouse platforms, and scalable data engineering practices. The ideal candidate should have experience leading high-performing engineering teams while driving platform scalability, reliability, observability, and AI-readiness across globally distributed environments. You will work closely with Product, Architecture, and Business stakeholders to build reusable platform capabilities and enable standardised, data-driven solutions across multiple regions and business domains.
Responsibilities:
Lead and mentor high-performing engineering teams focused on scalable data platform development.
Drive end-to-end ownership of platform capabilities, including architecture, development, deployment, reliability, and operations.
Partner with Product Managers and Architects to build scalable and reusable data platform solutions.
Design and implement modern cloud-native data architectures aligned with enterprise standards.
Build highly scalable batch and real-time data processing systems on GCP.
Drive engineering excellence across data engineering, DevOps, CI/CD, observability, and platform reliability.
Ensure systems are optimised for scalability, fault tolerance, performance, and cost efficiency.
Collaborate with global and cross-functional teams to enable reusable data products and platform services.
Lead initiatives around data governance, schema management, data quality, lineage, and platform standardisation.
Evaluate emerging technologies and lead proofs-of-concept for next-generation data platform capabilities.
Foster a strong engineering culture with a focus on ownership, accountability, innovation, and continuous improvement.
Enable AI-ready platform capabilities supporting MLOps, feature engineering, and ML workflows.
Requirements:
14+ years of overall experience with 8+ years in Data Engineering, Data Platforms, or Distributed Data Systems.
Strong experience leading large engineering teams in product-based or enterprise-scale environments.
Experience working in globally distributed and federated engineering organisations.
GCP and Data Platform Expertise:
Strong hands-on expertise in Google Cloud Platform (GCP), including: BigQuery, Data warehousing, Query optimization, Partitioning & clustering, Cost optimization, Dataflow (Apache Beam), Large-scale batch processing, Streaming pipelines, Fault-tolerant distributed processing, Pub/Sub, Real-time ingestion, Event-driven architecture, Streaming systems, Cloud Composer (Airflow), Workflow orchestration, DAG management, Pipeline scheduling, Google Cloud Storage (GCS), Data lake/lakehouse architectures, Staging and storage optimization.
Technical Expertise:
Strong understanding of modern Data Lakehouse architectures on GCP.
Expertise in building end-to-end scalable data pipelines from ingestion to transformation and serving layers.
Deep understanding of real-time streaming systems, schema evolution, event modelling, and distributed processing.
Strong programming expertise in Python and Spark (Scala preferred).
Hands-on experience implementing CI/CD pipelines for data platforms.
Strong understanding of analytical data modelling and optimisation techniques in BigQuery.
Experience with data contracts, schema governance, metadata management, and reusable data products.
Exposure to Data Mesh and event-driven platform architectures.
Familiarity with MLOps and AI-enabled data platforms, including: Feature engineering pipelines, Model training workflows, Inference pipelines, Vertex AI integration (preferred).
Reliability and Platform Engineering:
Strong focus on platform observability, monitoring, alerting, lineage, and data quality frameworks.
Experience managing cost-performance optimisation across GCP services.
Understanding of platform reliability engineering and operational excellence practices.
Experience building scalable, resilient, and highly available distributed systems.
Leadership and Stakeholder Management:
Proven ability to build and scale high-performing engineering teams.
Strong stakeholder management and cross-functional collaboration skills.
Ability to influence teams across multiple geographies and business units.
Excellent communication and leadership capabilities.
Strong product mindset with focus on platform adoption and reusable engineering capabilities.

Experience
14-18 yrs