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Open nowPosted today

AVP, Data Platform Engineering

STARHUB LTD.

Pay
SGD 16,000 – SGD 19,000 a Monthly
Where
Central, Singapore
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Your applicationOpen nowAVP, Data Platform EngineeringSTARHUB LTD. · Central, Singapore

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This job: posted today

The posting

Role Overview

As Assistant Vice President, Data Platform Engineering, you will own the strategy, architecture, and delivery of the data platform that powers StarHub's network, customer, and operational intelligence. This is the foundational engineering role of the AI & Intelligent Automation pillar: every AIOps, autonomous-operations, and network-quality outcome depends on the platform you build.

You will unify fragmented telemetry from RAN, Core, Transport, and Fixed Access alongside OSS/BSS and customer data into a governed, real-time data fabric. You will lead a team of data and platform engineers, embedding a product mindset that treats data as a first-class asset — discoverable, trustworthy, and AI-ready by design.

Key Responsibilities

1. Data Platform Strategy & Architecture

  • Platform Ownership: Define and deliver the end-to-end data platform — ingestion, storage, processing, and serving — spanning batch and streaming across the network and enterprise estate.
  • Lakehouse Architecture: Own a governed Lakehouse (Delta/Iceberg) with a medallion model, balancing cost & latency & technology, and scale for petabyte volumes.
  • Build vs. Buy: Set the technology roadmap across cloud-native and on-prem, making defensible trade-offs on managed services, open standards, and vendor lock-in.

2. Telecom Data & Streaming Intelligence

  • Network Telemetry Fabric: Engineer high-throughput pipelines for gNMI streaming, IPFIX, and CDR/xDR feeds, normalizing multi-vendor 4G/5G SA and XGS-PON data into a common model.
  • Real-Time Processing: Deliver sub-second streaming (Kafka/Flink) to power AIOps anomaly detection, closed-loop automation, and O-RAN rApp/xApp data needs.
  • OSS/BSS Integration: Consolidate OSS/BSS, fault, and performance-management sources into a unified observability layer for network quality and assurance.

3. Governance, Quality & Compliance

  • Data Governance: Establish catalog, lineage, and quality frameworks with clear ownership, ensuring PDPA compliance and auditable controls across the estate.
  • Security & Trust: Enforce zero-trust access, tokenization, and role-based controls aligned to IMDA expectations for sensitive customer and network data.
  • SLA-Backed Data Products: Treat pipelines as products with freshness, availability, and quality SLOs that downstream teams can depend on.

4. Future — Data Science & AI Enablement

  • Feature & Vector Infrastructure: Stand up feature stores and vector databases to serve ML models and RAG-based applications at production scale.
  • MLOps Foundation: Provide the training, serving, and monitoring substrate for AIOps and autonomous-network models — including LoRA/QLoRA fine-tuning and hallucination-mitigation workflows for network-domain LLMs.
  • Self-Service Analytics: Democratize governed data access so engineering and commercial teams can build models and insights without compromising control.
  • Emerging Direction: Position the platform for real-time digital-twin, generative-AI copilots for operations, and predictive network intelligence as the pillar matures.

Experience & Qualifications

  • Education: Bachelor's or Master's in Computer Science, Data Engineering, Telecommunications, or a related field. PhD in technical field is a big plus.
  • Experience: 12+ years in data / platform engineering, with at least 4 years leading teams; telecom or large-scale data environments strongly preferred.
  • Technical Depth: Deep expertise in distributed data systems, lakehouse architecture, and streaming (Kafka, Spark/Flink); hands-on with cloud-native data platforms.
  • Telecom Data: Familiarity with network telemetry (gNMI, IPFIX, CDR/xDR), OSS/BSS, and 5G SA / XGS-PON data models.
  • AI/ML Enablement: Working knowledge of MLOps, feature stores, vector databases, and the data foundations for RAG and model fine-tuning.
  • Governance: Practical command of data governance, lineage, and PDPA security controls.

Core Competencies

  • Architectural Judgement: Ability to make defensible platform trade-offs under real cost, latency, and scale constraints.
  • Product Mindset: Treats data as a governed product with clear consumers, SLOs, and lifecycle ownership.
  • Cross-Functional Leadership: Partners fluently with network engineering, AI/automation, security, and commercial stakeholders.
  • Forward Orientation: Builds today's platform with tomorrow's data-science and autonomous-network demands designed in.

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