• Understanding and aligning with business needs, identifying related data requirements • Participating in technical pre-sales activities, including effort estimation, and proposal creation • Architecting and planning robust data models and modern data platforms. • Acting as the senior technical lead for the development of cloud data solutions, with a focus on lakehouse architectures • Planning and leading testing activities • Leading and mentoring project teams (3-6 members)

• Act as a consultant on strategic business projects focused on data engineering using the Databricks platform; • Identify and implement best practices for high-volume data ingestion, ensuring performance and data availability; • Work on query optimization and data modeling to improve the efficiency of analytical queries; • Develop and maintain data pipelines (ETL/ELT) to integrate multiple sources, ensuring data quality and governance; • Design and implement Data Lakehouse architectures, applying the Medallion pattern (Bronze, Silver, Gold); • Support data modeling for modern Data Warehouses (Star Schema, Snowflake); • Explore best practices in Big Data environments, especially in the context of Databricks and Delta Lake; • Use open formats such as Delta Lake, Iceberg, Parquet and Avro for building and governing modern Lakehouse architectures; • Ensure data governance, including access control and versioning.

• Act as a Consultant on consulting projects, analyzing the data platform, identifying opportunities and proposing improvements; • Design, develop and deploy data pipelines into the Data Lake (Big Data); • Integrate various partner systems via APIs; • Develop, test and maintain database architectures and real-time or distributed systems; • Plan and execute data cleansing and remediation activities.

• Develop scalable data management and data processing architectures. • Manage data acquisition from API, batch, event or streaming sources. • Develop processes for data aggregation. • Design and develop data pre- and post-processing stages. • Plan and design for data governance, security, provenance and the over-all data lifecycle. • Leverage best-in-class cloud technologies to cater for OLTP and OLAP business needs. • Integrate ML models and Analytic components into the workflows (including MLOps). • Work closely with Data Science and Application Development teams in an agile development process.