Senior Data Engineer

Randstad (Schweiz) AG; London; On-site; GBP 50,000 - 70,000; Full time

Job summary

A leading company in biosensing technology is seeking a Data Engineer II to join their expanding Data Engineering team in London. In this role, you will be instrumental in developing and scaling data infrastructures that contribute to analytics and operational workflows. You will collaborate with a talented team from various disciplines, focusing on building reliable data pipelines and ensuring effective data management practices.

Qualifications

• 5+ years of experience in data engineering.
• Strong SQL skills and experience with relational and non-relational databases.
• Familiarity with CI/CD tools and data visualization.

Responsibilities

• Build and maintain scalable, reliable data pipelines.
• Design and implement data storage solutions.
• Develop reporting systems and dashboards for data-driven decision-making.

Skills

Python

SQL

Data Quality

System Observability

Containerization

Cloud Infrastructure

Data Processing Frameworks

Data Visualization

Tools

AWS

Azure

Looker

Power BI

Docker

GitHub Actions

Job description

Company Information

All the health information we need is within us. Just below the skin. SAVA is redefining the way people interact with their health by developing the most advanced biosensing technology science has to offer, capable of accessing bodily information in a painless, real-time and affordable way.

Description

Join Sava’s Data Engineering team at a pivotal stage of growth, contributing to the development and scaling of both, internal and customer-facing data infrastructures. As a Data Engineer II, you will help implement reliable data pipelines, storage solutions, and reporting systems that power analytical and operational workflows across the company. This is a unique opportunity to work in a multidisciplinary environment, collaborating with a top-tier team in engineering, science, and product to shape the foundation of Sava’s data architecture.

Responsibilities

• Build and maintain scalable, reliable data pipelines, ensuring efficient and accurate data ingestion, transformation, and delivery.
• Design, implement, and optimize data storage solutions using modern databases and cloud-native tools.
• Develop and maintain reporting systems and dashboards to support data-driven decision-making.
• Collaborate with backend and infrastructure teams to integrate data services with applications and customer-facing tools.
• Implement automated testing and validation processes for data workflows and pipelines.

Past Experience

• 5+ years of experience in data engineering.
• Proficiency in Python.
• Strong SQL skills and experience with both relational and non-relational databases (e.g., SQL, MongoDB).
• Familiarity with data visualization or reporting tools (e.g., Looker, Power BI, or similar).
• Familiarity with containerization and CI/CD tools (e.g., Docker, GitHub Actions).
• Knowledge of networking and cloud infrastructure (e.g., AWS, Azure).
• Experience with modern data processing frameworks (e.g., dbt, Apache Airflow, Spark, or similar).

Requirements

• A strong focus on system observability and data quality.
• Emphasis on rapid scalability of solutions (consider market ramp up when entering a new market)
• Relentless pursuit of system security.
• Adaptable mindset — open to using different tools and approaches depending on project needs.
• Ability to work & communicate across disciplines. Ability to translate concepts using analogies where possible. Disciplines you’ll be working closely with include Data Science, Mobile Engineering, Embedded Software through to other fringe disciplines like Manufacturing, Electronics, Sensor Development, and Mechanical Engineering.

Preferred

• Exposure to regulated environments (e.g., healthcare, finance) or compliance frameworks (e.g., HIPAA, SOC2, ISO 27001).
• Experience working with data residency constraints and multi-region architectures.
• Understanding of secure data handling practices and basic vulnerability concepts.
• Familiarity with model-based design approaches, including ER diagrams or data modeling tools.