• Development of custom solutions in the area of SAP ILM and data management • Implementation of compliance and ILM requirements and execution of technical analyses • Design of technical solution architectures in the area of SAP Data Management and ILM • Creation and optimization of reports and programs related to data protection and data volume management • Data cleansing and optimization, ensuring high data quality • Support for workshops and training sessions

Role Description At Dreamix, we are actively seeking a talented Databricks-certified Senior Data Engineer to join our team. We are on the lookout for an individual with a robust skill set in data engineering. If you are passionate about creating seamless data solutions, thrive in a collaborative environment, and are eager to contribute to the success of our data-driven initiatives, we invite you to apply. - Design and own scalable, production-grade data pipelines across the medallion architecture (ingestion, transformation, serving). - Define and enforce canonical data models aligned to business domains. - Architect pipelines for scalability, reliability, and cost efficiency, with strong focus on performance tuning (latency, partitioning, compute, SLAs). - Build and optimize data workflows using Databricks (Spark, Delta Lake, Delta Live Tables, Autoloader, Unity Catalog). - Establish robust data quality, validation, and testing frameworks across all pipeline layers. - Ensure reliability through proper handling of retries, idempotency, and failure scenarios. - Partner with data scientists, analysts, and business stakeholders to translate requirements into high-quality data products. - Develop and maintain data integrations and APIs to enable seamless data access and interoperability. - Implement data governance, security, and compliance best practices across the platform. - Mentor and support engineers by setting best practices, providing technical guidance, and contributing to a strong engineering culture. - Troubleshoot, optimize, and continuously improve data infrastructure and workflows based on evolving needs and technologies. Qualifications - A minimum of 5 years of relevant experience in data engineering. - Bachelor’s degree in Computer Science, Information Technology, or a related field. - Databricks certification is a must as well as extensive hands-on experience with Databricks (Spark, Delta Lake, Delta Live Tables, Autoloader, Unity Catalog) and modern big data ecosystems (e.g., Kafka). - Advanced proficiency in Python and SQL, with extensive experience building scalable ETL/ELT pipelines. - Experience designing and implementing data models (dimensional, entity-based, SCD) and efficient database schemas. - Deep understanding of data warehousing and modern Lakehouse/medallion architecture patterns. - Experience with cloud platforms (AWS, Azure, or Google Cloud) and their data services. - Experience building data products for consumption via APIs or analytics tools. - Strong understanding of data governance, access control, and regulated data environments. - Proven ability to define engineering standards, mentor engineers, and collaborate effectively in fast-paced environments. Benefits - A warm and supportive work environment where you can reach your full potential. - Flexible working hours that allow you to balance your work and personal life. - Unlimited home office to help you stay productive and focused. - Opportunities for professional development, including certifications and training. - Additional benefits for academic teaching and speaking engagements. - Knowledge-sharing sessions where you can learn from our Dreamix team. - Team and company-wide events that bring us together. - Amazing week-long summer office and winter office initiatives. - Additional health insurance to ensure your well-being.

• Support Snowflake-to-Databricks data migration activities • Design and implement scalable data pipelines in Snowflake and Databricks • Develop data ingestion and transformation processes from various sources • Migrate and optimize existing data workflows, ETL processes, and transformation logic • Build and maintain data transformations using SQL, Python, dbt, and other data engineering tools • Work with Databricks technologies, including Apache Spark-based data processing • Optimize pipelines for performance, scalability, cost efficiency, and maintainability • Automate data workflows and orchestration processes using tools such as Airflow or similar technologies • Collaborate with technical and business stakeholders to understand requirements and deliver effective data solutions • Ensure data quality and reliability throughout migration and development processes

• Design and ship production pipelines on Databricks, using a medallion architecture grounded in Data Vault 2.0 modeling patterns. • Build and harden the curated and presentation layers—driving donor lifetime value, retention, lapse risk, and campaign ROI. • Resolve identity across products to compute values on one trustworthy record. • Move toward near-real-time data and partner with architects on Change Data Capture. • Extend existing tracing and AI lifecycle tooling into ETL. • Partner with AI and product engineers to ensure data is properly shaped for applications. • Use AI tools in daily development tasks. • Raise the bar on engineering standards, addressing testing, idempotency, documentation, and security.