Data Engineer ETL responsible for AI model data cleaning and development. Leading projects in a hybrid work environment in Singapore.
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)
Job title
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