Data & AI Engineering

Position Overview 

We are looking for a motivated Data & AI Engineer to join our data platform team at Opella. The role will focus on building scalable, cloud-native data pipelines and analytics-ready datasets that support enterprise reporting, business intelligence, machine learning, and AI-driven insights. 

The ideal candidate should have strong hands-on experience with Snowflake, Python, SQL, dbt, Airflow, and cloud platforms such as AWS or Azure. The candidate should also have exposure to AI-ready data platforms, including semantic layers, metadata-driven analytics, prompt engineering, natural language-to-SQL, and agentic AI frameworks. 

Experience in CPG, retail, e-commerce, commercial analytics, finance, or supply chain data will be a strong advantage. 

Key Responsibilities 

Data Engineering & Platform Development 

• Build and maintain scalable data pipelines using Python, SQL, Snowflake, dbt, and Airflow.  

• Ingest, transform, and model structured and semi-structured data from enterprise systems, APIs, files, and external sources.  

• Develop Snowflake-based data warehouses, data marts, and analytics-ready datasets.  

• Optimize Snowflake queries, data models, warehouse usage, and storage patterns for performance and cost efficiency.  

• Build reusable, modular, and metadata-driven data pipelines to support multiple analytics and AI use cases.  

• Troubleshoot data pipeline failures, performance issues, and data quality problems.  

AI & Agentic Analytics Enablement 

• Support development of AI-ready data products for natural language analytics, intelligent query generation, insight generation, and recommendations.  

• Contribute to semantic layers and metadata models that define KPIs, dimensions, entities, business rules, and data relationships.  

• Support agentic AI frameworks, including retrieval, tool orchestration, workflow agents, prompt templates, and business-rule grounding.  

• Assist with prompt engineering patterns for intent detection, entity extraction, SQL generation, insight summarization, and recommendation generation.  

• Enable AI use cases such as RAG, natural language-to-SQL, diagnostic agents, forecasting assistants, and recommendation engines.  

Data Quality, Governance & Collaboration 

• Implement data validation, reconciliation, monitoring, and quality checks across pipelines.  

• Support data governance, metadata management, documentation, lineage, and role-based access controls.  

• Work closely with data architects, data scientists, analysts, business users, and platform teams.  

• Participate in architecture discussions, code reviews, deployment activities, and documentation.  

Required Qualifications 

• 8+ years of experience in data engineering, analytics engineering, or data platform roles.  

• Strong hands-on experience with Snowflake, including data modeling, query optimization, and pipeline development.  

• Strong proficiency in Python and advanced SQL.  

• Hands-on experience with dbt for transformation, testing, documentation, and modular data modeling.  

• Experience with workflow orchestration tools such as Airflow.  

• Experience with AWS or Azure cloud data platforms.  

• Experience using Git and CI/CD workflows.  

• Strong understanding of ETL/ELT, data warehousing, dimensional modeling, and analytics engineering.  

• Experience with data quality, governance, metadata, and production data pipeline best practices.  

• Strong problem-solving and communication skills.  

Preferred Qualifications 

• Exposure to AI, GenAI, ML, or advanced analytics data pipelines.  

• Understanding of agentic AI frameworks, including tool orchestration, retrieval, workflow agents, and business-rule grounding.  

• Experience with prompt engineering for analytics use cases such as intent detection, SQL generation, insight summarization, and recommendations.  

• Familiarity with semantic layers, business glossaries, KPI definitions, metadata-driven analytics, and natural language-to-SQL.  

• Exposure to CPG, retail, e-commerce, commercial analytics, finance, or supply chain analytics.  

• Familiarity with business KPIs such as sales, revenue, gross margin, pricing, promotions, inventory, forecast, market share, sell-in, sell-out, and customer/channel performance.  

• Knowledge of data governance, lineage, data quality frameworks, and cloud-native deployment practices