This Data Engineer role involves building and maintaining data pipelines, ensuring data quality, model design, and supporting AI data preparation in an enterprise data platform environment.
As a Data Engineer at Quantios, you will play a critical role in building and maintaining the data foundation for Quantios Insights, our enterprise data platform that integrates data from all Quantios products and powers advanced analytics, MCP-based automation, and AI-driven use cases. You will design and operate robust data pipelines, ensure data quality and governance, and collaborate closely with developers, architects, and business stakeholders to deliver curated, reliable, and scalable datasets.
Job Responsibilities:
Data Pipeline Engineering
- Design, build, and maintain scalable ETL/ELT pipelines using Microsoft Fabric, Databricks, or Snowflake.
- Develop data ingestion processes for structured, semi-structured, and event-based data from Quantios products.
- Build scalable dataflows using Python, SQL, PySpark, or similar technologies.
- Implement automated data refresh, validation, and monitoring processes.
- Ensure pipelines are efficient, cost-effective, and aligned with enterprise data architecture standards.
Data Modelling & Lakehouse Architecture
- Implement lakehouse/medallion architecture (bronze, silver, gold layers).
- Design and maintain semantic data models for analytics and AI-ready datasets.
- Optimize datasets for Power BI, Fabric semantic models, and other analytics tools.
- Collaborate with architects to maintain modelling standards and best practices.
Data Quality & Governance
- Implement data validation, schema enforcement, and profiling to maintain high-quality datasets.
- Maintain data lineage using governance tools such as Fabric Data Governance, Databricks Unity Catalog, or Snowflake.
- Support metadata management and cataloguing tools such as Purview.
- Ensure compliance with data security, governance, and regulatory standards.
AI & RAG Data Preparation
- Prepare structured and unstructured datasets for AI, RAG pipelines, and LLM evaluation.
- Collaborate with LLMOps engineers to provide high-quality training and validation datasets.
- Develop curated datasets for AI agents, semantic search, and internal experimentation.
- Support vectorisation workflows, chunking strategies, and semantic data preparation.
Platform Delivery & Customer Enablement
- Support customer deployments of Quantios Insights across enterprise data platforms.
- Contribute to reference architectures and platform configuration guidelines.
- Work with Product Owners and Professional Services to streamline customer data onboarding.
- Assist customers in aligning their data environments with Quantios product structures.
Collaboration & Agile Delivery
- Work closely with architects, product owners, and engineering teams to deliver data solutions.
- Translate analytical and AI requirements into scalable data engineering solutions.
- Participate in Agile ceremonies including backlog refinement, estimation, and sprint planning.
Continuous Improvement
- Stay updated with emerging technologies in data engineering, analytics, and AI platforms.
- Identify opportunities to improve data reliability, performance, and automation.
- Contribute to internal best practices and promote high-quality engineering standards.
Job Requirements:
- Bachelor’s degree in Computer Science, Data Engineering, Data Science, or a related field; or equivalent industry experience.
- 4+ years of experience in data engineering, preferably within cloud-based or enterprise environments.
- Hands-on experience with one or more: Microsoft Fabric, Azure Databricks, Snowflake.
- Strong skills in Python and SQL, with exposure to PySpark or Spark SQL.
- Experience with Azure Data Lake Storage, Delta Lake, ELT/ETL pipelines, and medallion architecture.
- Familiarity with Power BI, Fabric semantic models, or equivalent BI modelling tools.
- Practical experience integrating with CI/CD tools, especially Azure DevOps.
- Understanding of data governance, cataloguing, and metadata management (e.g., Purview, Unity Catalog).
- Exposure to AI-related data preparation (RAG datasets, embeddings, unstructured text processing) is a plus.
- Excellent problem-solving skills, ability to work across teams, and strong communication skills.
Top Skills
Azure Data Lake Storage
Azure Devops
Databricks
Delta Lake
Microsoft Fabric
Power BI
Purview
Pyspark
Python
Snowflake
SQL
Unity Catalog
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