Senior Specialist, Observability Data Engineer

Role Overview

A Data Engineer is responsible for designing, building, and maintaining robust data pipelines/reports and infrastructure that facilitates the collection, storage, and processing of large datasets. They ensure data/insight is accessible, reliable, and optimized for analysis by collaborating with data scientists and analysts. Key tasks include data ingestion, integration, ETL (Extract, Transform, Load) processes, report design and managing databases and cloud-based systems. Data engineers play a crucial role in enabling data-driven decision-making and ensuring data quality across organizations.

What You Will Do

Design, implement, and maintain complex data engineering solutions to acquire and prepare data. Create and maintain data pipelines to connect data within and between data stores, applications and organisations. Monitor performance statistics, create reports, Identify and investigate complex problems, issues and recommend corrective actions.

Why It Might Be a Fit

7+ years of developing data pipelines & data infrastructure, ideally within a drug development or life sciences context. Demonstrated expertise in delivering large-scale information management technology solutions encompassing data integration and self-service analytics enablement. Strong working experience of at least one data ingestion and one data curation tool (can be open source, cloud-native or commercial/enterprise).

Requirements

• Bachelors’ degree in Information Technology, Computer Science or any Technology stream.
• 7+ years of developing data pipelines & data infrastructure, ideally within a drug development or life sciences context.
• Demonstrated expertise in delivering large-scale information management technology solutions encompassing data integration and self-service analytics enablement.
• Strong working experience of at least one data ingestion and one data curation tool (can be open source, cloud-native or commercial/enterprise).
• Strong working knowledge of at least one large-scale data processing technology (e.g., High-performance computing, distributed computing), databases, and underlying technology (cloud or on-prem environments, containerization, distributed storage & databases).
• Practical experience working with the Databricks platform and a good understanding of its core principles (including Delta Lake, Spark workloads, notebook workflows, cluster management).
• Cloud-native, ideally AWS certified.
• Very good skills in Python and SQL. Good interpersonal and communication skills (verbal and written).
• Proven record of delivering high-quality results.
• Product and customer-centric approach.
• Innovative thinking, experimental mindset.
• Experience with data observability tooling or frameworks is an advantage.
• Handson Apache Airflow experience (deployment, DAG troubleshooting, scaling) is an advantage.

Benefits

• Flexible Work Arrangements
• Hybrid
• Valid Driving License