Senior Data Engineer

Responsibilities As a senior data engineer, you will be responsible for ideation, architecture, design and development of our key data platform components Create and maintain essential data platform SDKs and libraries, adhering to industry best practices Design and develop connector frameworks and modern connectors to source data from disparate systems both on-prem and cloud Design and optimize data storage, processing, and querying performance for large-scale datasets using industry best practices while keeping costs in check Design and develop data quality frameworks and processes to ensure the accuracy and reliability of data Collaborate with data scientists, analysts, and cross functional teams to design data models, database schemas and data storage solutions Proactively identify and contribute towards platform resiliency Design and develop observability and data governance frameworks and practices Stay up to date with the latest data on engineering trends, technologies, and best practices Drive the deployment and release cycles, ensuring a robust and scalable platform Partner with AI enablement teams across the organization as well as KION IT Cloud Infrastructure, AI Platform, and security teams to ensure AI/ML capabilities align with IT framework and guidelines within KION Partner with Business Transformation Data Management teams to align on master data Requirements 5+ years of proven experience in modern cloud data engineering and software engineering Proven ability to build end-to-end data platforms and data services (beyond ETL) Strong cloud experience, preferably GCP; Azure (ADLS) and Databricks are strong pluses Hands‑on experience with Databricks (Delta Lake, Spark, ML/ETL workflows) Experience working in multi-cloud environments (GCP / Azure) Proficiency with platforms such as BigQuery, Dataflow, Dataform, Cloud Run, DBT, Dataproc, SQL, Python, Airflow, Pub/Sub, and equivalent Azure tooling such as ADLS, Azure Functions, and Databricks Experience with microservices architectures (Kubernetes, Docker) Deep experience with batch and streaming data infrastructures Strong hands‑on experience with metadata management, data catalogs, data lineage, data quality, and data observability frameworks Strong understanding of data modeling, data architecture, and data governance Solid experience with DataOps, CI/CD, and test automation Excellent experience with observability tooling Experience building data platforms supporting AI use cases and machine learning Production level experience with universal semantic layers Production level experience with implementing either 3rd party or open-source metadata management platforms Production level experience with data platform resiliency Experience building large-scale data platforms on Azure, including Azure Data Lake and Databricks #J-18808-Ljbffr Jobtailor