Data Engineer - 3

We are seeking a Senior Data Engineer (Individual Contributor) to build and scale the data infrastructure that powers analytics, machine learning, and business intelligence across the Data Science and Analytics (DSA) team. This is a highly hands-on technical role that combines deep expertise in data engineering, distributed systems, and cloud data platforms to enable reliable, scalable data solutions across Rebel's fast-evolving business landscape. You will be responsible for designing and maintaining robust data pipelines, scalable data models, and efficient data platforms that support high-impact use cases across the organisation. You will work closely with data scientists, analysts, product managers, and engineering teams to ensure data is reliable, accessible, and production-ready for critical decision-making systems.

Our work spans across areas of inventory forecasting, order predictions, marketing optimisation, delivery time prediction, personalisation, customer insights, product recommendations, supply chain planning, and capacity optimisation. We are looking for someone who enjoys solving complex data engineering challenges, has strong ownership, and is excited to build scalable systems that power data-driven decisions at scale.

At Rebel, the Data Science and Analytics team builds and maintains the data infrastructure, pipelines, and data models that power analytics, machine learning systems, reporting automation, and real-time dashboards used across the organisation. We interact with teams across every business area, such as supply, operations, demand generation, D2C, finance, brands, customer delight, central planning, and many more.

Responsibilities:

• Design, build, and maintain scalable data pipelines and data infrastructure to ingest, transform, and serve data across batch and near-real-time systems.
• Develop reliable ETL / ELT workflows to integrate data from multiple operational systems into centralised data platforms.
• Design and maintain clean, scalable data models that support analytics, BI dashboards, and machine learning pipelines.
• Partner closely with data scientists and analysts to build and maintain feature pipelines and enable seamless productionisation of ML models.
• Ensure data quality, observability, and monitoring across pipelines through validation checks, alerts, and performance optimisation.
• Optimise pipelines and data platforms for performance, scalability, and cost efficiency in a cloud environment.
• Collaborate with cross-functional teams to translate business requirements into scalable data engineering solutions.

Requirements:

• 4-8 years of industry experience in data engineering, data platform engineering, or related roles.
• Strong experience designing and building scalable ETL / ELT pipelines and distributed data systems.
• Hands-on experience with Python and SQL for building production-grade data workflows.
• Experience working with cloud platforms such as AWS and services like S3 Glue, EMR, Lambda, or similar tools.
• Experience with data orchestration frameworks such as Airflow, Prefect, or Dagster.
• Strong understanding of data modelling, schema design, and data warehouse concepts (dimensional modelling, star schema).
• Experience with modern data warehouses or lakehouse architectures such as Redshift, Snowflake, BigQuery, or Delta Lake.
• Ability to work closely with cross-functional teams and translate data requirements into scalable engineering solutions.

Nice to Have:

• Experience with real-time or streaming data pipelines using tools like Kafka, Kinesis, or Spark Streaming.
• Exposure to ML pipelines, feature stores, or MLOps workflows supporting production ML systems.
• Experience building data observability and monitoring systems for large-scale data pipelines.
• Familiarity with fast-paced start-up environments and agile ways of working.

Skills

ELT, ETL, Python, SQL