Data Engineer – Build & Operate Unified Data Platform

About the role

Design, build, and operate a unified data platform and domain pipelines that enable scalable, trusted, and monetisable data products. You will work across batch and real‑time ingestion, cloud‑native storage, and analytics enablement.

Key responsibilities

• Design and implement batch and real‑time ingestion pipelines from internal and external sources.
• Build and maintain cloud‑native data lake and warehouse architectures.
• Ensure high availability, performance, and cost efficiency of data infrastructure.
• Implement automated data quality checks, observability, and SLA monitoring.
• Support master data management, metadata, lineage, and access controls.
• Embed security, privacy, and compliance controls into the platform by design.
• Optimise datasets and pipelines for analytics, machine‑learning training, and API consumption.
• Collaborate closely with Data Scientists and ML Engineers to support feature and model needs.
• Enable self‑service data access for downstream teams.
• Continuously improve performance, scalability, and reliability of pipelines.
• Introduce modern tooling and standards to reduce operational overhead.
• Contribute to the long‑term platform roadmap and AI readiness.

Required profile

• 5+ years of experience in data engineering or platform roles.
• Strong experience with SQL, Python, Spark, and cloud platforms (AWS, Azure, or GCP).
• Experience operating data platforms in production at scale.
• Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related field.
• MS in Big Data, AI, or relevant fields is a plus.
• Certifications such as PMI‑ACP, PMP, SAFe, CSM, or equivalent are beneficial.

Required skills

• SQL
• Python
• Spark
• AWS
• Azure
• GCP
• Data lake
• Data warehouse
• Batch ingestion
• Real‑time ingestion
• Data quality checks
• Observability
• SLA monitoring
• Master data management
• Metadata management
• Lineage tracking
• Access controls
• Security
• Privacy
• Compliance
• Analytics
• Machine learning
• API consumption