Role Description - Work closely with technical leads and client teams to fully demonstrate the benefits of GCP technology. - As Google Cloud Platform (GCP) data migration engineer, responsible for overseeing the migration of data from various sources to Google Cloud Platform. - This role involves: - Analyzing existing data structures. - Designing migration strategies. - Implementing data migration processes. - Ensuring data integrity and security throughout the migration process. - Providing technical guidance and support to stakeholders. - Optimizing data migration processes for efficiency and scalability. - Documenting migration procedures and best practices. - Define and implement cloud-based solutions in line with industry best practices and enterprise architecture guidelines. Qualifications - Strong technical skills in GCP services, data migration tools, and new database technologies. - Experience with scripting languages. - Excellent problem-solving, communication, and collaboration abilities. - Database experience with either RDBM or NOSQL. - Technical writing experience in preparing and presenting technical material to a variety of audiences. - Experience in working in, and with, Agile delivery teams. Requirements - GCP experience. - Experience working on enterprise-level migration projects. - Experience in large-scale, secure, and high availability solutions in cloud environments such as Google Cloud (GCP). - Strong experience in Data Engineering, Data modeling, DBT is preferred. Qualities We Look For - Passionate about delivering high-quality commercial software products and platforms to market. - Team player; enjoy working with each other and clients. - Strong understanding of modern software engineering processes. - Client-focused and passionate about delivering business value. - Able to communicate clearly and efficiently with a variety of audiences including developers, clients, customers, partners, and executives. - Flexible and willing to use the right technology for each problem in the context of timelines and business goals. - Ability to complete the job regardless of the circumstance.

• 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