About Dateonic & the project
Big Data and AI are in our DNA, with modern cloud data platforms at the core of our innovation. Founded by engineers and architects passionate about tackling complex data challenges, Dateonic combines industry-leading technical expertise with a product-focused approach to turn raw data into actionable intelligence. Our culture is driven by three core values: Passion, Community, and Commitment. We love tough problems, support each other through close collaboration, and deliver what we promise without taking shortcuts.
In this outsourcing role, you will represent Dateonic on a long-term initiative with a global leader in experience innovation. Our partner operates at the intersection of data, AI, creativity, and technology to build modern digital platforms for world-renowned enterprise brands. Joining this project allows you to work on high-impact international data solutions while enjoying the support, engineering community, and technical mentorship of the Dateonic team.
The role
We are looking for a skilled Data Engineer with strong hands-on experience in Snowflake and dbt within cloud environments, primarily Microsoft Azure. You will be responsible for building, maintaining, and optimising production-grade ELT pipelines, designing robust data models, and implementing software engineering best practices across data workflows.
Beyond writing clean code, you will work autonomously to investigate production issues, monitor data quality, and communicate effectively with cross-functional teams, architects, and analysts in an international setting.
Responsibilities
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Design, develop, and maintain robust batch and scheduled ELT data pipelines in production environments.
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Build and manage dbt models, automated tests, documentation, and dependency workflows adhering to modern data modelling standards.
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Optimise Snowflake queries, warehouse sizing, and data models for high performance, scalability, and cost efficiency across large datasets.
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Ensure pipeline reliability and observability by implementing automated monitoring, alerting, error handling, and recovery mechanisms.
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Apply software engineering standards to data pipelines, including Git version control, CI/CD automation, and testing frameworks.
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Perform root cause analysis on production issues and data anomalies to deliver permanent technical and architectural resolutions.
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Collaborate with client teams, technical architects, and data analysts across international project streams while taking ownership of assigned engineering tasks.
Must-have requirements
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Strong hands-on experience with Snowflake in production environments, including query tuning, workload management, and cost optimisation.
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Proven expertise in dbt, covering model development, testing, documentation, and deployment practices.
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Advanced SQL skills with a solid understanding of dimensional data modelling and layered data architectures.
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Practical Python programming experience for data ingestion, automation, and API integrations.
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Demonstrable experience building, scheduling, monitoring, and troubleshooting production ELT pipelines.
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Working knowledge of Microsoft Azure and cloud-based data services.
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Solid understanding of software engineering best practices, including Git and CI/CD pipelines applied to data workflows.
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Strong problem-solving skills, ability to work autonomously, and fluent professional English for international team collaboration.
Nice to have
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Experience with orchestration tools such as Dagster, Airflow, or cloud-native equivalents.
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Exposure to Infrastructure as Code using Terraform.
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Familiarity with Agentic AI, Snowflake Cortex, or AI-enabled data products.
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Hands-on experience with AWS data services (such as S3, RDS, Secrets Manager, and CloudWatch).
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Exposure to Apache Kafka or other streaming and event-driven data patterns.
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Experience with BI and reporting environments such as Power BI.
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