Manager, Data Engineer

Data Engineer – Demand Analytics & Intelligent Data Layer

About the Role We are looking for a forward-thinking Data Engineer to architect and build the data layer that powers the organization’s ability to translate demand signals into business actions. In this role, you will design scalable data pipelines bridging Google Cloud Platform (GCP) and Tencent Cloud, establish a governed lakehouse with a semantic layer, and pioneer intelligent agents that enable self-service analytics and autonomous operations.

Key Responsibilities

• Data Pipeline & Lakehouse Architecture
  Design, build, and maintain scalable ETL/ELT pipelines to ingest, transform, and synchronize data from GCP to Tencent Cloud. Leverage modern orchestration frameworks and AI-assisted tooling to optimize pipeline performance, reliability, and cost-efficiency.

· Semantic Layer & Metric Governance

Build a unified semantic layer that standardizes metrics, dimensions, and business logic across demand domains. Define canonical data models, enforce data contracts and quality SLAs, and ensure that every downstream report and dashboard is grounded in trusted, consistent definitions. Explore knowledge graph and ontology modeling to enrich the semantic layer for advanced relationship-aware analytics.

• Self-Service analytics Agent
  Develop AI agents that enable business users to ask questions in plain language and receive trusted metrics and insights. The agent reasons over the semantic layer and knowledge graph to resolve business intent, validate against governed definitions, and generate optimized queries—ensuring every answer is consistent with enterprise data standards.
• Data Content Observability
  Implement comprehensive observability frameworks to monitor data quality, freshness, schema drift, and lineage across the entire data estate. Deploy automated anomaly detection and alerting to ensure data trustworthiness.
• Self-Healing Data Agents
  Develop and deploy AI-powered self-healing agents that continuously monitor the data platform, proactively detect anomalies (e.g., pipeline failures, quality degradation, schema drift, cost spikes), and autonomously execute remediation workflows such as retries, rerouting, or resource scaling with minimal human intervention.
• Cross-Cloud & Cross-Functional Collaboration
  Partner with global engineering, AI/ML, and business teams to align data architecture with enterprise goals. Ensure security, compliance, and governance standards are met across both GCP and Tencent Cloud environments.

Qualifications

· Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.

· 5+ years of experience in data engineering, with a proven track record of building production-grade data pipelines and lakehouses.

· Deep expertise in GCP (BigQuery, Dataflow, Pub/Sub, Cloud Storage) and Tencent Cloud (TCHouse, TencentDB, COS, Serverless Cloud Functions).

· Strong proficiency in Python, TypeScript, Java, SQL, and modern data orchestration tools (e.g., Airflow, Prefect, Dagster, or Tencent Cloud equivalents).

· Hands-on experience designing semantic layers and metric stores (e.g., dbt, Cube.dev, Looker ML, or similar). capable of converting ambiguous business requirements into clear, well-structured data models.

· Practical experience building LLM-powered applications or agents; familiarity with Deepagents, LlamaIndex, OpenAI API, or equivalent frameworks.

· Capable of writing clear and concise documentation: data dictionary, runbooks, pipeline specs, onboarding guides, etc.

· Excellent communication skills in English; ability to collaborate effectively across global, multicultural teams.

Preferred Qualifications (Plus)

· Experience with Infrastructure-as-Code (Terraform) and CI/CD for Data Pipelines
Familiarity with provisioning cloud infrastructure via Terraform and automating data pipeline deployments through CI/CD workflows is a strong plus. While not required, this capability will accelerate your ability to build reproducible, version-controlled, and operationally mature data infrastructure across our multi-cloud environment.

· Practical experience with graph databases (e.g., Neo4j, TGDB) and ontology engines (e.g., GraphDB,Stardog) for knowledge graph construction and ontology modeling.

· Experience with multi-cloud or hybrid-cloud data architectures.

· Familiarity with data compliance and localization requirements (e.g., PIPL, GDPR).

· Contributions to open-source data tools or active presence in the data engineering community.