With our client we are building the next generation data platform that will leverage AI/ML techniques to help redefine how our customers bring life-saving and life-changing products to market. To enable this, we are looking for a Senior Data Engineer to support our Data Science and Machine Learning teams.

• Drive the evolution and maintenance of existing data pipelines, ensuring scalability, performance, and maintainability.

• Simplify and optimize data models to improve pipeline efficiency and reduce complexity.

• Be part of consolidated efforts to reduce redundant or unnecessary data pipelines, promoting reusability and standardization.

• Help to finalise the transition from batch to stream-based data processing using Apache Flink, ensuring minimal disruption and optimal performance.

• Collaborate with cross-functional teams to design and deliver high-quality, domain-agnostic datasets for AI/ML applications.

• Ensure data reliability, consistency, and integrity to support high-impact Data and ML product development.

• Actively collaborate within the data engineering team and across adjacent teams (e.g., data science, platform engineering, product) to align on goals, share knowledge, and drive innovation.

• 7+ years in a Data Engineering role

• Expertise working with distributed data technologies (e.g. Hadoop, MapReduce, Spark, Flink, Kafka, etc.) for building efficient & large-scale data pipelines.

• Software Engineering proficiency in at least one high-level programming language (Java, Scala, Python or equivalent).

• Experience building stream-processing applications using Apache Flink, Spark-Streaming, Apache Storm, Kafka Streams or others.

• Knowledge of multi-dimensional modeling like start schema, snowflakes, normalized and de-normalized models.

• Knowledge of flexible, scalable data models addressing a wide variety of consumption patterns including random-access, sequential access including necessary optimizations like bucketing,aggregating, sharding.

• Expertise in one or more NoSQL database (Neo4J, Mongo DB, Cassandra, HBase, DynamoDB, Big Table etc.).