AI Data Platform Lead

NEWBRIDGE ALLIANCE PTE. LTD.; Singapore; On-site; SGD 180,000 - 300,000; Full time

Job summary

NEWBRIDGE ALLIANCE PTE. LTD. in Singapore is seeking a Staff / Lead Data Engineer (AI-Native) to architect petabyte-scale data pipelines feeding real-time bidding, recommendations, and analytics.

You will balance hands-on coding with mentoring a small team, focusing on reliability, scalability, and AI-ready data foundations built on Spark, Flink, Databricks, and modern lakehouse architectures.

Qualifications

• 8+ years building large-scale distributed data systems
• Strong SQL and production coding in Python/Scala/Java
• Deep hands-on with Spark/Kafka/Flink or equivalent
• Experience with cloud data platforms (Databricks/Snowflake/BigQuery) and lakehouse architectures

Responsibilities

• Hands-on design and shipping of critical pipelines feeding real-time bidding and ML workloads
• Mentor a small engineer pod while contributing as an IC
• Ensure data quality, observability, and lineage for Tier-0 datasets

Skills

SQL

Python/Scala/Java

Distributed data

Big data concepts

Education

Bachelor's degree in CS or related

Tools

Databricks

Snowflake

BigQuery

Kafka

Spark

Job description

Our client is a high-scale global consumer platform serving millions of users daily. They are rebuilding their data foundation as an AI-native platform to power the next generation of programmatic advertising, personalized discovery, and marketplace intelligence.

We are hiring a hands-on Staff / Lead Data Engineer (AI-Native) to architect petabyte-scale systems that feed real-time bidding, recommendation models, and LLM-powered analytics products.

Role Mandate: 80% Hands-on Engineering / 20% Technical Leadership

This is NOT a pure management role. You will be a player-coach - designing and shipping critical pipelines yourself while mentoring a small pod of engineers. Strong individual contributors with no formal people management experience but with deep technical depth are strongly encouraged to apply.

What You Will Build

1. AI-Native Data Foundation at Scale

Design, build, and operate AI-ready batch + streaming ETL/ELT pipelines ingesting 100TB+ daily from ad servers, mobile SDKs, transactional systems, and 3rd-party APIs. Build for LLM and ML consumption from day one.

2. Real-Time & Agentic Data Systems

Develop low-latency streaming jobs using Spark Streaming, Flink, or Kafka Streams for real-time use cases: fraud detection, bid optimization, dynamic pricing, and real-time personalization. Enable online inference and agentic decisioning.

3. Lakehouse for AI & Analytics

Model and optimize massive datasets on a modern lakehouse [Databricks / Snowflake / BigQuery] to serve BI, embedded analytics, and AI/ML workloads with a focus on performance, cost, and feature freshness.

4. Data Products for AI

Build reliable data products for Data Science & ML: feature stores [Feast / Tecton], vector stores for semantic search & RAG, training datasets with point-in-time correctness, and online-offline parity.

5. Reliability for Tier-0 AI Systems

Own data quality, observability, anomaly detection, and lineage for Tier-0 datasets that directly power revenue, user experience, and model performance.

Required Qualifications

• 8+ years building large-scale distributed data systems for programmatic advertising, digital media, marketplaces, or large consumer internet platforms, supporting 50+ downstream engineers / analysts / scientists
• Expert-level SQL and strong production coding in Python, Scala, or Java
• Deep hands-on experience with distributed processing: Spark, Kafka, Flink or equivalent
• Proven expertise with cloud data platforms: Databricks, Snowflake, BigQuery, Redshift, AWS / GCP / Azure
• Strong data modeling for AI: dimensional, Data Vault, lakehouse, medallion architecture optimized for analytics and ML
• Track record handling high-volume, semi-structured, late-arriving event data at TB-PB scale

Preferred - You Stand Out If You Have:

• AI-Native Data Engineering: Experience building AI-native infrastructure - feature platforms, vector DBs, LLM eval pipelines, RAG data pipelines, unstructured data processing for GenAI, AI agent memory/context infrastructure.
• AdTech Domain: DSP/SSP internals, impression/click/conversion pipelines, SKAN, MMM/MTA, Conversions API, identity resolution & signal loss mitigation
• Ecommerce / Marketplace Domain: Product catalog & taxonomy at scale, pricing experimentation, inventory forecasting, seller analytics, search & recommendation data
• ML Data Infra: Feature Stores, online-offline parity, training data infrastructure, model monitoring data loops
• Leadership & Impact: Experience leading 0-to-1 architecture for critical domains like real-time advertising or attribution. Demonstrated impact on cost optimization of $1M+ in annual cloud spend.
• Privacy & Trust: Knowledge of privacy-enhancing tech: differential privacy, data clean rooms, secure multi-party compute.