Data Team Lead

Tech Lead, Data Engineering

Location: District 3, Ho Chi Minh City, Viet Nam

Work model: Onsite

Employment Type: Full-time

1. Role Overview

The Tech Lead, Data Engineering is the technical head of the Data Stream and the

architect behind Glo's data foundation. You will lead a team responsible for ensuring

that every AI-powered feature, personalization engine, and business insight

operates on validated, high-performance data infrastructure.

Your scope spans the full data lifecycle, from event ingestion through transformation,

storage, and delivery, up to the Feature Store and Vector DB level, where you can

provide a clean hand-off to the Platform stream for application-layer development.

You will translate complex requirements from the AI and Automation stream into

production-grade technical systems, while simultaneously ensuring that BI and

analytics teams have access to high-trust, well-modeled data products.

This is a hands-on leadership role. You will personally architect core data systems in

ClickHouse and Qdrant, define engineering standards for the team, and decompose

abstract AI requirements into executable work for Data Engineering and Analytics

contributors.

2. Key Responsibilities

Data Architecture & Infrastructure

Design and maintain Glo's core data architecture, including OLAP storage

structures in ClickHouse and vector search infrastructure in Qdrant.

Own the event data lifecycle from ingestion (RudderStack) through transformation,

modeling, and delivery to downstream consumers.

Implement and optimize data modeling patterns (Star Schema, Data Vault 2.0)

appropriate to each use case.

Deploy and manage data platform components using Infrastructure-as-Code

(Terraform or Pulumi) within AWS, with CI/CD best practices.

AI Enablement

Serve as the primary technical lead for the embedding lifecycle, from generation

through indexing, retrieval, and performance optimization in Qdrant.

Translate AI & Automation stream requirements into granular, technically feasible

tasks for the engineering team.

Build and maintain the feature store infrastructure that powers Glo's

personalization and recommendation systems. Ensure clean system boundaries: own everything up to the Feature Store / Vector

DB level, with well-defined contracts for the Platform stream.

Data Quality & Trust

Lead the creation and maintenance of high-trust data products, implementing

automated validation including freshness checks, row count drift detection, and

anomaly triage.

Architect and maintain validated data marts that serve as the single source of truth

for BI and analytics.

Establish and enforce data quality standards, observability practices, and incident

response protocols across the Data Stream.

Team Leadership

Lead, mentor, and grow a team of data engineers and analytics engineers.

Drive technical alignment across the Data, AI, and Platform streams.

Decompose complex projects into well-scoped work with clear acceptance criteria.

Make architectural decisions transparent through documentation, ADRs, and open

communication.

3. Required Qualifications

English communication requirement.

Education: Bachelor's degree in Computer Science, Engineering, Mathematics, or

a related field (or equivalent experience).

Experience: 7+ years in data engineering or analytics engineering, with

demonstrated ownership of production-grade pipelines and data warehouses.

SQL: Expert-level analytical SQL, you can write, optimize, and debug complex

queries without hesitation.

Python: Strong proficiency for pipeline development, automation, and tooling.

Data Modeling: Hands-on experience with dimensional modeling (Star Schema)

and/or Data Vault patterns in an OLAP context.Cloud Infrastructure: Production

experience with AWS data services; comfort with Infrastructure-as-Code (Terraform

or Pulumi).

Data Transformation: Experience with modern transformation tooling (dbt or

equivalent) in a warehouse-centric architecture.

Data Quality: Practical experience implementing data observability, whether

through tools like Soda, Great Expectations, or custom-built solutions.

Leadership: Track record of leading technical projects end-to-end and mentoring

engineers.

4. Preferred Qualifications

ClickHouse: Direct experience with ClickHouse or comparable column-oriented

OLAP databases (e.g., Apache Druid, DuckDB at scale).

Vector Databases: Familiarity with embedding workflows and vector search

systems (Qdrant, Pinecone, Weaviate, or similar).

Event Streaming / CDP: Experience with event-driven architectures or customer

data platforms (RudderStack, Segment, or similar).

Containerization: Working knowledge of Docker; experience with container

orchestration (ECS, Kubernetes) is a plus.

ML/AI Infrastructure: Exposure to feature stores, embedding pipelines, or ML

serving infrastructure.