What are the responsibilities and job description for the Data Engineer – Cloud and AI position at Innovien Solutions?
Job Description:
Our client is transforming how they deliver data products — and you’ll be joining the Data & Analytics product organization at the center of it. Working within a Scaled SAFe Agile environment alongside other data engineers, scientists, analysts, stewards, modelers, and scrum masters, your primary focus will be Service Growth & Transformation: supporting the data orchestration, transformation, and consumption needs that drive key service business decisions. What makes this role extraordinary is what the organization has already proven — a two-person team (one Product Manager, one AI-enabled Data Engineer) can deliver what traditionally required a team of six, in 20% of the time, with production-ready output. You are not filling a seat; you are the catalyst for scaling that model across a global enterprise.
- 7 years’ experience building and maintaining scalable data pipelines in a cloud environment – specifically within Microsoft Azure (Data Factory, Databricks/PySpark) and Snowflake
- Extensive SQL skills including window functions, CTEs, query optimization, and schema design, paired with strong Python ability for data transformation, API integration, automation scripting, and programmatic interaction with external services and LLM APIs
- Experience using AI tools (ChatGPT or Gemini preferred) as a daily force multiplier to accelerate code writing, debugging, and solution design — combined with working knowledge of prompt engineering to produce structured, repeatable, and reliable AI output
- Hands-on experience calling LLM APIs (OpenAI, Azure OpenAI, Gemini) programmatically to build automation workflows, with a solid understanding of Retrieval-Augmented Generation (RAG) patterns and how to apply them against enterprise data
- Proven ability to design and build autonomous, multi-step AI agents that plan, reason, and act across tools, APIs, and data systems that create orchestrated, goal-driven workflows in production
• Understanding of AI ethics, governance, and compliance in enterprise data environments
- Experience working in global, matrixed organizations with multiple business units or regions
- Certifications: SnowPro Core or Advanced, Microsoft Azure Data Engineer, or Azure AI Engineer
- Bilingual in French or Spanish
- Build and maintain scalable ELT/ETL pipelines in Snowflake and Azure (Data Factory, Databricks) that make high-quality data available for service business decision-making.
- Design and optimize Snowflake data models, leveraging dynamic tables, streams, tasks, and Snowpark, to support fast, reliable data consumption across business units.
- Develop and deploy AI-powered automation workflows using the OpenAI API, Google Gemini, and Azure OpenAI Service to reduce manual effort and accelerate delivery.
- Build and productionize Agentic AI workflows, autonomous, multi-step agents that act across tools, APIs, and data systems, scaling the lean pod delivery model the organization has already proven.
- Integrate LLMs into data pipelines for use cases such as intelligent summarization, anomaly detection, and natural language querying of enterprise data.
- Support the service growth initiatives, including the field service rising program and repair acceleration effort, by delivering production-ready data products on aggressive timelines.
- Implement data quality frameworks, monitoring, and alerting to ensure pipeline reliability and data integrity across a global, multi-region environment.
- Build semantic layers and data models that power Power BI reporting and enable self-service data access for analysts and business stakeholders.
- Operate within the Scaled SAFe Agile delivery framework, contributing to PI planning, sprint ceremonies, and release train activities as an embedded team member.
- Act as an internal AI catalyst, modeling an AI-native engineering mindset and demonstrating to a traditionally manufacturing-oriented organization what modern, high-velocity data delivery looks like.