We are seeking a talented and experienced Data Lakehouse Engineer to join the Data Solutions domain in EMEA. In this role, you will design, build, and optimize scalable lakehouse and data platform solutions that enable data-driven decision-making across the organization. You will collaborate closely with cross-functional teams to create resilient ingestion and transformation pipelines while ensuring reliability, performance, and high-quality data across the ecosystem.
What you will do:
- Contribute to the design and implementation of lakehouse integration architectures, including data flows, process flows, and ELT/ETL patterns.
- Build, optimize, and maintain scalable data pipelines for ingestion, processing, and storage across data lake and lakehouse layers.
- Implement and uphold data governance, security, lineage, and compliance best practices throughout the data lifecycle.
- Monitor, troubleshoot, and fine‑tune data workflows, compute jobs, and storage layers to ensure high performance and reliability.
- Provide production support, ensuring robust system observability with proactive detection and resolution of issues.
- Collaborate with platform, analytics, and engineering teams to ensure efficient data modeling, table design, and storage optimization (e.g., Iceberg/Delta patterns).
What you need to have:
- 5+ years of experience in a data engineering or similar role within a fast‑paced, enterprise-scale environment.
- Bachelor’s or Master’s degree in Computer Science, Data Engineering, or a related technical field.
- Strong Proficiency in SQL and Data Integration Platforms such as IBM Streamsets or Informatica with thorough understanding of data transformation and orchestration processes.
- Extensive Experience with AWS services, including IAM, Lambda, EKS, S3 (Data Lake), EMR (PySpark), MWAA, and Lakehouse technologies (Apache Iceberg).
- Proven expertise with Snowflake, including performance tuning and efficient query design.Experience with batch pipelines (e.g., StreamSets) and streaming technologies (e.g., Kafka).
- Nice to have - Data Transformation Frameworks such as DBT.
- Experience with both batch (StreamSets) and streaming (Kafka) data solutions.
- Familiarity with data modelling concepts (Inmon, Kimball, Data Vault, etc.).
- Exposure to CI/CD or Infrastructure-as-code (Terraform or any other) is a plus.
- Application Performance Monitoring Framework experience is a plus.
- Knowledge of data quality and security best practices is advantageous.
- Ability to understand complex architecture solutions and make sound estimates for the implementation.
- Strong attention to detail, with a proactive approach to identifying and resolving problems.
- A genuine interest in emerging data technologies and a curiosity about continuous improvements
- Excellent verbal and written communication skills.
- Attention to detail mindset, proactively identify problems & evaluate solutions
- Comfort working in an AI‑empowered, automation-driven environment
