Senior Consultant - Data Engineering

Senior Consultant – Data Engineering

Role Purpose

We are looking for a Data Engineering Consultant with strong expertise in Snowflake and Cortex AI to support retail data transformation initiatives. This role focuses on enabling advanced analytics across customer, sales, inventory, and supply chain domains, and leveraging AI-driven insights to improve business outcomes such as demand forecasting, personalization, and operational efficiency.

Key Responsibilities

1. Design and implement scalable data platforms on Snowflake for retail analytics
2. Build and optimize ELT pipelines for retail data (POS, e-commerce, CRM, inventory, supply chain)
3. Leverage Snowflake Cortex to enable AI-driven use cases such as: Customer segmentation & personalization, Product recommendations, promo analysis
4. Integrate data from multiple retail systems (ERP, OMS, WMS, e-commerce platforms)
5. Develop data models for key retail metrics (sales, margins, inventory turnover, basket analysis)
6. Ensure data quality, governance, and compliance across datasets
7. Optimize Snowflake performance and cost for large-scale retail workloads
8. Collaborate with business stakeholders (merchandising, marketing, supply chain teams)
9. Enable near real-time analytics for inventory and order tracking

Required Qualifications & Experience

1. 5+ years of experience in Data Engineering, preferably in retail or e-commerce
2. Strong hands-on experience with Snowflake Data Cloud
3. Experience with Snowflake Cortex (AI/ML functions, LLM-powered analytics)
4. Advanced SQL and strong Python skills
5. Experience with ETL/ELT tools (dbt, Airflow, etc.)
6. Strong understanding of retail data models and KPIs
7. Experience with cloud platforms (AWS, Azure, or GCP)
8. Experience with BI tools (Tableau, Power BI, Looker)
9. Snowflake or cloud certifications (good to have)

Core Competencies

1. Strong analytical and problem-solving mindset
2. Ability to translate retail business needs into data solutions
3. Excellent communication with both technical and business teams
4. Agile delivery and stakeholder management

Key Performance Indicators (KPIs)

1. Data Platform Performance and Success
2. Data Quality & Reliability KPIs
3. Performance improvements ( data sync speed, batch jobs, sync time)
4. Reduction in time to generate business reports
5. SOPs and policies compliance