Choosing Capgemini means choosing a company where you will be empowered to shape your career in the way you’d like, where you’ll be supported and inspired by a collaborative community of colleagues around the world, and where you’ll be able to reimagine what’s possible. Join us and help the world’s leading organizations unlock the value of technology and build a more sustainable, more inclusive world.

BASIC QUALIFICATIONS:

• Proven track record of a minimum of 4 years in management, in a space with strong focus on large scale data processing and instrumentation.
• Strong coding background, ideally in Java/ Python / Scala
• Strong working knowledge of engineering best practices & big data ecosystem..
• Experience in at least one big data product: Databricks, Elasticsearch, Snowflake
• Experience building batch / real time data pipelines for production systems.
• Experience with Relational and Non-Relational DBs like DB2, MongoDB
• Experience with various data formats: Parquet, CSV, JSON, XML, Relational Data
• Strong familiarity with Kafka, Spark, Hadoop, Iceberg, Airflow, Data Modeling, relational databases, columnar databases
• Previous working experience in large scale distributed systems.
• Strong familiarity with software engineering principles, including object-oriented and functional programming paradigms, design patterns, and code quality practices.
• Excellent communication skills, with the ability to effectively collaborate with cross-functional teams and explain technical concepts to non-technical stakeholders.

DESIRED QUALIFICATIONS:

• Experience with Rest based applications
• Experience with Databricks/ Delta Lake
• Experience with client reference data sourcing from vendors

• Collaborating and leading part of a cross-functional Agile team to create and enhance software for data ingestion and entity resolution platform
• Expertise in application, data and infrastructure architecture disciplines
• Working with large, complex data sets from a variety of sources
• Participate in the rapid development of user-driven prototypes to identify technical options and inform multiple architectural approaches
• Building efficient storage and search functions over structured and unstructured data
• Utilizing programming languages Python, Java, Scala, Relational and NoSQL databases
• Learning newer technologies for entity resolution such as Quantexa platform