Lead Data Engineer
Company Overview
TECQMIND is an end-to-end AI professional services and staffing partner. We focus on the "how"—building the teams and infrastructure required to turn AI potential into production-scale reality. We specialize in providing fractional leadership, curated engineering talent, and custom AI implementations that include robust data architecture and continuous performance optimization.
The Role
As a Lead Data Engineer at TECQMIND, you are the architect of the data ecosystems that power our AI solutions. You will lead the design and implementation of scalable data pipelines, "Model Gardens," and robust data lakes. This role requires a visionary approach to data—ensuring that the information feeding our custom models is clean, secure, and highly performant.
You will act as a technical pillar, coordinating closely with our Taiwan-based engineering partners to ensure that the infrastructure we build is world-class and future-proof.
Key Responsibilities
Architectural Leadership: Design and oversee the construction of end-to-end data architectures, including Data Lakes, Warehouses, and "Model Gardens" for proprietary AI deployment.
Scalable Pipeline Development: Build and maintain high-throughput ETL/ELT pipelines that handle diverse data types (structured, semi-structured, and unstructured) for AI training and inference.
System Integration: Ensure seamless "Full-Stack Integration" by building the bridges between client data sources and AI models, ensuring data flows reliably across the ecosystem.
Strategic Optimization: Implement "Continuous Optimization" workflows, including automated data quality checks, performance monitoring, and security auditing to prevent data drift and system degradation.
Global Team Coordination: Lead technical workflows and code reviews with our overseas tech partners in Taiwan, ensuring architectural standards are met and local requirements are integrated.
Stakeholder Collaboration: Partner with our Fractional CTOs to define technical roadmaps and provide clients with high-level guidance on data strategy and IP protection.
Required Qualifications
Experience: 6+ years in Data Engineering, with at least 2 years in a lead or senior capacity.
Tech Stack Proficiency: Mastery of Python and SQL. Extensive experience with Big Data technologies (e.g., Spark, Snowflake, dbt).
Orchestration & Workflow: Expert knowledge of orchestration tools like Airflow, Prefect, or Dagster.
Cloud Infrastructure: Proven experience designing data environments in AWS, GCP, or Azure (experience with Model Gardens/Vertex AI/SageMaker is a major plus).
Vector Infrastructure: Practical experience with Vector Databases (e.g., Pinecone, Weaviate, Milvus) for RAG-based AI applications.
Leadership: Demonstrated ability to mentor engineers and manage complex technical projects from conception to production.
Highly Desired: Preferred Qualifications
Bilingual Proficiency: Professional fluency in both English and Mandarin (written and verbal) to facilitate high-level architectural collaboration with our Taiwan-based teams.
Global Operations: Experience managing or leading distributed technical teams across different time zones.
Security & Governance: Deep understanding of data privacy laws and security frameworks (SOC2, GDPR) as they apply to proprietary AI models.