About this role
Who we are
We are seeking an accomplished and detail-oriented Snowflake Data Engineer. The successful candidate will bring deep expertise in data engineering, ETL/ELT pipelines, and cloud-native data platforms, with a strong focus on Snowflake. This role is critical in building and optimising modern data ecosystems that enable data-driven decision making, advanced analytics, and AI capabilities for our clients.
This is a code-heavy software engineering role focused on creating resilient streaming pipelines, handling event states, and orchestrating complex workflows across multiple cloud providers.
As a trusted practitioner, you will collaborate with architects, developers, and analysts to design, implement, and maintain secure and high-performing data pipelines. You will thrive in a collaborative, client-facing environment, with a passion for solving complex data challenges, driving innovation, and ensuring the seamless delivery of data solutions.
What you'll be doing
- Pipeline Development (ETL/ELT): Write clean, production-grade code to extract, transform, and load massive data volumes from relational databases, APIs, and microservices into cloud data warehouses
- EDA Implementation: Build and maintain high-throughput, low-latency stream processing pipelines. Solve real-world streaming challenges like idempotency (preventing duplicate events), backpressure handling, and out-of-order data processing
- Multi-Cloud Integration: Deploy, configure, and monitor data processing jobs natively across different cloud ecosystems based on cost, latency, and performance needs
- Orchestration & Automation: Develop automated, event-triggered workflow graphs to monitor data lineage, system health, and pipeline alerts
- Performance Tuning: Optimize distributed compute queries (e.g., Spark clusters, Snowflake warehouse sizing) to manage cloud spending efficiently
What you'll bring along
- BSc/MSc in Computer Science, Data Engineering, or related field; Snowflake certifications (SnowPro Core, Advanced) highly desirable
- Experience: Minimum 4–6+ years of active software engineering or data engineering experience
- Languages: Expert proficiency in Python, SQL, Scala, or Java
- Cloud & Tools Stack: Hands-on development experience using Apache Spark / PySpark, Apache Flink, Apache Kafka, or Azure Event Hubs
- Orchestration & DevOps: Strong skills with Apache Airflow or Dagster, CI/CD deployment pipelines, and Docker/Kubernetes containerization