AVP, Data Platform Engineering

STARHUB LTD.; Singapore; In loco; SGD 240.000 - 420.000; Tempo pieno

Descrizione del lavoro

STARHUB LTD. is seeking an AVP-level leader to own the strategy, architecture, and delivery of StarHub's data platform. You will unify telemetry from RAN, Core, Transport, and Fixed Access with OSS/BSS and customer data into a governed, real-time data fabric.

You will lead a team of data and platform engineers, treating data as a first-class product with strong SLOs, trust, and AI-ready readiness for analytics and automation.

Competenze

• Bachelor's or Master's in Computer Science, Data Engineering, Telecommunications, or related field.
• PhD in a technical field is a big plus.

Mansioni

• Own strategy, architecture, and delivery of StarHub's data platform, powering network, customer, and operational intelligence.
• Lead high-throughput data pipelines (gNMI streaming, IPFIX, CDR/xDR) and real-time processing (Kafka/Flink).
• Consolidate OSS/BSS, fault, and performance sources into a unified observability layer.
• Establish data governance, lineage, quality, and PDPA controls; enforce zero-trust and role-based access.

Conoscenze

Data platform leadership

Distributed data systems

Lakehouse architecture

Streaming pipelines

gNMI/IPFIX/CDR data models

MLOps knowledge

PDPA governance

Formazione

Bachelor's or Master's in CS/Engineering

PhD a big plus

Descrizione del lavoro

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.