Databricks Data Engineer (ICH Europe)

Databricks Community of Practice Join our Databricks Community of Practice in Poland, where delivery excellence is at the core of everything we do. We design and build modern data products as well as advanced GenAI and agentic solutions powered by Databricks. As part of a global network of 9,000+ AI experts and data scientists, we collaborate with leading technology partners—including Databricks, AWS, Google Cloud, Microsoft, Snowflake, and SAS—to deliver scalable, enterprise-grade data solutions that generate measurable business impact.   · Design and implement scalable end-to-end data pipelines (ETL/ELT) using Databricks (batch and streaming), including modern orchestration patterns such as Delta Live Tables · Develop and maintain data models using Lakehouse and Medallion architecture (Bronze, Silver, Gold layers). · Build robust data transformations using PySpark, Spark SQL, and Delta Lake. · Integrate diverse cloud and enterprise data sources into unified, high-quality, analytics-ready datasets. · Collaborate with architects, analysts, and data scientists to deliver production-grade data products. · Implement DevOps and DataOps practices, including Git-based version control, CI/CD pipelines, and testing of data workflows. HERE’S WHAT YOU’LL NEED:

Requirements (Cumulative): At least 2 years of hands-on experience working with relational or analytical databases, applying SQL, Python or Spark for development, testing, debugging, and performance optimization in production environments. Within that experience, at least 1 year practical experience designing or implementing ETL/ELT processes using Databricks and Apache Spark in cloud-based environments. Exposure to data modelling and architecture: practical experience creating conceptual, logical and physical data models using dimensional, relational or Data Vault techniques in analytical environments. Technical Skills: Databricks & Spark : Hands-on experience working with Databricks platform, including Delta Lake and Lakehouse architecture concepts. Practical knowledge of Apache Spark (PySpark, Spark SQL), including batch and streaming processing. Understanding of Medallion architecture design patterns. Databases & SQL: Strong hands-on SQL skills in analytical and distributed data environments (e.g., Spark SQL). Ability to profile, tune, and optimize SQL queries for large-scale data processing workloads. Data Modeling & ETL Solid understanding of dimensional modelling; ability to translate business requirements into conceptual, logical and physical models. Hands-on experience building scalable ELT/ETL pipelines in modern cloud-native environments. Cloud & Big Data (Experience or Interest): Experience with, or strong interest in, processing and integrating data on major cloud platforms (GCP, Azure, AWS). Familiarity with cloud storage, managed databases and serverless / data pipeline services is desirable. Understanding of CI/CD pipelines and version control (Git) in data engineering projects Nice to Have: Experience with Databricks Workflows, Unity Catalog, Auto Loader, Structured Streaming. Knowledge of streaming technologies (e.g., Kafka). Experience with dbt, MLflow, Feature Stores, or Infrastructure as Code (e.g., Terraform). Exposure to data quality frameworks and monitoring solutions. Experience supporting AI/GenAI use cases (e.g., LLM data pipelines, vector databases, AI-ready data preparation). Programming & Analytics: Practical scripting or programming skills (SQL, Python, Spark) to support data transformation, automation and basic analytics. Ability to translate complex business requirements into robust, scalable data solutions. Soft Skills & Language : Strong analytical thinking, problem-solving and attention to detail. Professional working proficiency in English and effective communication with technical and non-technical stakeholders. Willingness to travel to client locations across Europe as required. Research indicates that some