Senior Consultant – Data Engineering at NFQ ASIA PTE. LTD. – Bangkok

Gradion helps enterprises modernize their technology foundations—migrating, securing, and scaling critical systems in the cloud and on-premise.

We partner with clients across e-commerce, travel, financial services, manufacturing, and technology to deliver real transformation, not just platforms.

We are seeking a Senior Data Engineering Consultant with deep experience in data platforms, data pipelines, and machine learning enablement.

You will help clients in DACH and APAC transform their data infrastructure—modernizing legacy systems, building governed data platforms, and operationalizing AI/ML models.

You will work closely with Gradion’s cloud and AI consultants to design end-to-end data architectures that balance scalability, governance, cost, and innovation.

• Advise senior client stakeholders on modern data architecture, cloud migration strategies, and the secure, compliant use of data for business value.
• Define clear roadmaps for clients to transition from legacy data warehouses to scalable cloud-native data platforms (e.g., Data Lakes, Lakehouses).
• Design data pipelines and structures that enable clients to monetize data assets and derive actionable insights.
• Conduct data maturity assessments and define target-state architectures and roadmaps.
• Communicate complex data and AI topics in clear business language to executives and stakeholders.

• Lead the design and implementation of robust, scalable, and cost-efficient data infrastructure (data lakehouse, data mesh, or centralized warehouse) on major hyperscaler platforms (AWS, Azure, GCP).
• Develop and optimize high-throughput data pipelines using modern ELT/ETL tools (e.g., Spark, Flink, Kafka) to handle large data volumes and integrate disparate data sources.
• Build, deploy, and manage production-ready machine learning AI/ML pipelines (MLOps) including feature stores, ML model registries, model training, workflows (e.g., MLflow, Vertex AI, SageMaker, Azure ML), serving and monitoring.
• Explore and implement data engineering infrastructure required to develop and deploy Small Language Models and other applied AI solutions within client environments for specific client use cases.
• Establish data governance, lineage, and compliance controls to ensure trustworthy AI and regulatory readiness.
• Contribute to Gradion’s internal frameworks for data platform modernization and AI readiness.

• Define and implement data governance frameworks aligned with ISO, FINMA, GDPR, or MedTech requirements.
• Embed data security, masking, and access control into pipelines and platform layers.
• Help clients design policy-as-code and automated compliance guardrails for data and AI systems.
• Conduct technical and architectural assessments (data platform Health Checks) to identify bottlenecks, security gaps, and cost inefficiencies.

• 7+ years of experience in data engineering, data architecture, or ML platform engineering roles.
• Strong background in ETL/ELT, data lake/warehouse architecture, and distributed data processing.
• Hands-on experience with one or more cloud data ecosystems (AWS, Azure, GCP, Snowflake, Databricks, BigQuery, Synapse, etc.).
• Proficiency in Python and SQL; experience with modern frameworks such as Spark, Airflow, dbt, Kafka.
• Familiarity with containerization and orchestration (Docker, Kubernetes).
• Experience designing and implementing MLOps principles and tooling (e.g., Kubeflow, MLflow, SageMaker, Azure ML). or integrating data pipelines with AI/ML workloads.
• Understanding of data security, compliance, and governance frameworks (ISO, GDPR, SOC2).
• Consulting mindset, able to translate technical depth into client value and communicate clearly with business stakeholders.
• Excellent communication, presentation, and stakeholder management skills, comfortable working with both technical teams and C-level executives (English proficiency, German a plus).

• Experience with data monetization, data products, or real-time analytics.
• Familiarity with LLM/SLM architectures, vector databases, or retrieval-augmented generation (RAG) patterns.
• Experience with Databricks, Snowflake, or other modern data warehousing/lakehouse platforms.
• Familiarity with distributed processing frameworks (e.g., Spark).
• A Master's degree in Computer Science, Data Science, or a related quantitative field.
• Experience in regulated industries (finance, healthcare, MedTech) or with cross-border data environments (EU/US/APAC).
• Certifications such as AWS Data Analytics, Azure Data Engineer, or GCP Professional Data Engineer are a plus.