Position Overview
We are seeking a versatile, Hybrid Data Analytics & Engineering Consultant to bridge the gap between complex data infrastructure, strategic business consulting, and modern software development.
In this role, you won’t just build data pipelines in isolation; you will operate as a FDE. You will embed deeply with business units to understand their toughest challenges, architect enterprise-grade data platforms using modern cloud stacks, and deploy production-ready AI and data applications that drive immediate business value.
Core Responsibilities
AI & Data Engineering
Design, build, and optimize scalable data pipelines to ingest, transform, and store structured and unstructured data.
Develop and integrate production-grade AI and machine learning pipelines, including large language model orchestration, retrieval-augmented generation frameworks, and vector database management.
Implement robust data governance, data quality monitoring, and master data management practices.
Data Analytics Consulting
Act as a trusted advisor to stakeholders, translating vague business problems into structured data requirements.
Design high-impact business intelligence dashboards, semantic layers, and predictive models that drive executive decision-making.
Conduct workshops and enablement sessions to drive data literacy and tool adoption across non-technical teams.
Forward Deployment Engineering
Deploy, configure, and maximize the value of modern ecosystem platforms—such as Databricks, Microsoft Fabric, and Snowflake—directly within business environments.
Create integrated, repeatable architectural patterns tailored to specific departmental needs.
Act as the tactical "boots on the ground" to rapidly prototype and scale high-value data products from concept to production.
Software Development
Write clean, maintainable, and well-tested code for data applications and infrastructure.
Apply rigorous DevOps and DataOps principles, including continuous integration and continuous deployment Git workflows, containerization, and automated testing.
Build lightweight application programming interfaces or microservices to expose data models and AI capabilities to downstream applications.
Qualifications & Requirements
Technical Skills
Data & AI Engineering: Strong experience with PySpark, Delta Lake, orchestration tools, and AI frameworks.
Cloud Platform Mastery: Deep, hands-on experience with at least one major modern data ecosystem, such as Databricks, Microsoft Fabric, or Snowflake.
Software Engineering: Proficiency in Python and SQL, with a solid understanding of Git, deployment pipelines, and writing unit tests.
Analytics & BI: Expertise in designing semantic models and building dashboards in modern business intelligence tools.
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