About the Role
We are looking for an experienced Senior Backend & Data Platform Engineer to join our engineering team and contribute to the development of scalable backend services and data infrastructure for digital healthcare and connected technology applications.
The successful candidate will be responsible for designing and implementing backend architectures, data pipelines, integration services, and data-management solutions.
This position requires strong capabilities in both backend software engineering and data engineering, particularly in data ingestion, ETL/ELT processes, database architecture, distributed systems, and reliable data integration.
The candidate will collaborate closely with software developers, embedded engineers, and machine-learning specialists to establish robust data infrastructure and support future data-driven applications.
We are open to different programming languages, frameworks, and infrastructure technologies. Strong engineering fundamentals and practical implementation experience are the primary selection criteria.

Key Responsibilities
Backend Architecture & Development
Design, develop, and maintain scalable backend services and APIs.
Establish modular and maintainable software architectures.
Implement RESTful APIs, asynchronous services, and integration interfaces.
Apply appropriate backend design patterns and distributed-system principles.
Ensure service performance, reliability, security, and scalability.

Data Engineering & Pipeline Development
Design and implement data ingestion and processing pipelines.
Develop ETL/ELT workflows for data extraction, transformation, validation, and integration.
Implement data normalization, synchronization, and quality-control mechanisms.
Handle missing, duplicated, delayed, and inconsistent data.
Develop reliable processing workflows for structured, semi-structured, and time-series data.
Establish appropriate data traceability and processing mechanisms.

Database Architecture & System Integration
Design and implement suitable database architectures and data models.
Work with relational, time-series, and other appropriate data-storage technologies.
Optimize database structures, queries, and data-access mechanisms.
Develop reliable integration interfaces for different software systems and data sources.
Support data consistency, integrity, historical storage, and retrieval.

Collaboration with AI & Software Engineering Teams
Collaborate with machine-learning specialists to understand and implement data requirements.
Translate analytical and machine-learning requirements into practical data engineering solutions.
Support the preparation of structured datasets for machine-learning applications.
Work with software and embedded engineers on technical interfaces and data-exchange requirements.
Support end-to-end system integration and troubleshooting.

Security & Infrastructure
Apply secure software development and data-management practices.
Implement appropriate access controls and data-protection mechanisms.
Support logging, monitoring, backup, and recovery processes.
Collaborate with DevOps teams on deployment and infrastructure automation.
Contribute to performance optimization and system reliability.

Required Qualifications
Bachelor's or Master's degree in Computer Science, Software Engineering, Data Engineering, or a related discipline, or equivalent professional experience.
At least 5 years of relevant experience in backend development and data engineering.
Strong professional experience in backend software development.
Practical experience designing and implementing data pipelines.
Strong understanding of ETL/ELT and data-processing workflows.
Experience with database design, data modeling, and SQL.
Experience developing RESTful APIs and backend services.
Understanding of distributed systems and asynchronous communication.
Experience with data validation, transformation, and integration.
Strong problem-solving and system-design capabilities.

Preferred Qualifications
Experience with one or more of the following is advantageous:
Backend: Python, C#, Java, Go, Node.js, or equivalent languages and frameworks.
Databases: PostgreSQL, SQL Server, MySQL, TimescaleDB, InfluxDB, or similar technologies.
Data Engineering: ETL/ELT frameworks, Apache Airflow, Dagster, or similar orchestration tools.
Messaging: Apache Kafka, RabbitMQ, MQTT, and other messaging technologies.
Architecture: Microservices, distributed systems, event-driven architectures, and data integration.
Infrastructure: Docker, Kubernetes, cloud infrastructure, and CI/CD.
Experience with healthcare, medical technology, IoT, or time-series data platforms.
Experience preparing datasets for machine-learning applications.
Technology flexibility: No specific programming language, framework, database, or cloud platform is mandatory. Demonstrated capabilities in backend engineering, data architecture, and pipeline development are the primary requirements.

Personal & Professional Competencies
Strong architectural and analytical thinking.
Ability to understand complex data flows and integration requirements.
Technical ownership and accountability.
Effective communication with software, embedded, and machine-learning engineers.
Strong troubleshooting and problem-solving abilities.
Commitment to documentation, data quality, and software maintainability.
Ability to work independently and collaboratively.

What We Offer
The opportunity to develop modern data infrastructure and backend systems.
Collaboration with software engineering and AI specialists.
Involvement in digital healthcare and data-driven technology initiatives.
Professional growth in distributed systems, data engineering, and backend architecture.
Position Objective: Design and develop scalable backend services and reliable data infrastructure to support the integration, processing, management, and utilization of data across digital healthcare and connected technology applications.