Data Tech Lead (SQL, Python, Data Modeling, AWS)

Công ty TNHH Stravo Vietnam

Ứng tuyển

Tòa nhà Pearl 5, 5 Lê Quý Đôn, Xuân Hòa, TP Hồ Chí Minh

Tại văn phòng

Đăng 11 giờ trước

Kỹ năng:

Data Engineer Leadership AWS Data modeling Python SQL

Chuyên môn:

Kỹ sư Dữ liệu

Lĩnh vực:

Sản Phẩm Phần Mềm và Dịch Vụ Web

Mô tả công việc

About the Role

The Tech Lead, Data Engineering is the technical head of the Data Stream and the architect behind our 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, providing a clean handoff to the Platform stream. You will translate complex AI and Automation requirements into production-grade systems, while ensuring BI and analytics teams have access to high-trust, well-modeled data products.

Key Responsibilities

Data Architecture & Infrastructure
- Design and maintain our 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
- Serve as the primary technical lead for the embedding lifecycle — from generation through indexing, retrieval, and performance optimization in Qdrant
- Build and maintain the feature store infrastructure powering Glo’s personalization and recommendation systems
- Translate AI & Automation stream requirements into granular, technically feasible tasks for the engineering team
- 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

Yêu cầu công việc

Required Qualifications

Experience: 7+ years in data engineering or analytics engineering, with demonstrated ownership of productiongrade pipelines and data warehouses
SQL: Expert-level analytical SQL — 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 via Soda, Great Expectations, or custom-built solutions
Leadership: Track record of leading technical projects end-to-end and mentoring engineers
Language: Professional proficiency in English
Education: Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related field — or equivalent experience

Preferred Qualifications

ClickHouse: Direct experience with ClickHouse or comparable column-oriented OLAP databases (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 ECS or Kubernetes is a plus
ML / AI Infrastructure: Exposure to feature stores, embedding pipelines, or ML serving infrastructure