Hybrid Senior Consultant – Data Engineering

at GRADION

• Job title: Senior Consultant – Data Engineering
• Job type: Full Time
• Experience level: Senior
• Salary: Not specified
• Degree requirement: Bachelor's Degree
• Tech skills: Airflow, AWS, Azure, BigQuery, Cloud, Docker, ETL, Google Cloud Platform, Kafka, Kubernetes, Python, Spark, SQL
• Location requirements: Hybrid, Ho Chi Minh City, Vietnam

About the role

• Senior Data Engineering Consultant at Gradion transforming data infrastructure for global clients. Modernizing legacy systems and operationalizing AI/ML solutions with cloud and AI consultants.

Responsibilities

• 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.

Requirements

• 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).
• Desired
• 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.

Benefits

• A laptop is provided
• Community Tech activities
• A fun & dynamic environment with freedom to be creative
• Modern office with flexible and relaxing spaces
• Performance bonus (up to 2-month salary)
• Performance review 2 times/year
• Extra Premium Healthcare & Annual Health-check
• 15 days of annual leave
• Working time: Monday – Friday (9 AM – 6 PM)