Founding Data Platform Engineer

Responsibilities:

• Design and build scalable data platforms and distributed data processing systems.
• Develop and optimise large-scale batch and real-time data pipelines.
• Work with technologies such as Apache Spark, Kafka, ClickHouse, Delta Lake, Databricks, or similar data platforms.
• Build data processing frameworks for TB-scale datasets and optimise computation, storage, and query performance.
• Design precomputation, caching, and data-serving frameworks to reduce analytics latency and infrastructure costs.
• Build reliable workflows for data ingestion, transformation, processing, and delivery.
• Work on real-time analytics and low-latency data processing use cases.
• Improve data platform reliability through monitoring, anomaly detection, testing, observability, and controlled production deployments.
• Design data models and platform capabilities that support analytics, reporting, and AI/ML use cases.
• Collaborate with Data Science, ML, Product, and Engineering teams to build data infrastructure for new products and use cases.
• Take ownership of platform components from architecture and design through production deployment and optimisation.

Requirements:

• 6-10 years of experience in Data Engineering, Data Platform, Distributed Systems, or Analytics Infrastructure.
• Strong programming skills in Python, Scala, Java, C++, or C#.
• Strong hands-on experience with Apache Spark or other distributed data processing frameworks.
• Strong SQL and understanding of data modelling, databases, and data processing concepts.
• Experience working with large-scale data pipelines and distributed data systems.
• Experience with one or more of ClickHouse, Kafka, Delta Lake, Databricks, Hadoop, Flink, Snowflake, BigQuery, or similar technologies.
• Understanding of batch processing, real-time/streaming processing, data warehousing, and data lake architectures.
• Experience with cloud platforms such as Azure, AWS, or GCP.