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Tier-1 brand, mid-level generalist title, metro location, and broad Azure/Snowflake/BI requirements drive high competition.
Core Azure/Snowflake data engineering skills are transferable, but manufacturing consulting domain knowledge moderately raises specificity.
Explicit 4-7 years and mandatory Azure, Databricks, Snowflake, BI, and Python skills create strict filters.
Build and manage data engineering solutions in Azure cloud environment, including scalable data pipelines and futuristic data architecture.
Deliver business intelligence solutions by synthesizing requirements, performing data modeling, exploratory data analysis, and KPI visualization using tools like Power BI and Tableau.
Consult with business clients to design controlled data-driven experiments and develop insights for decision-making from complex, high-volume datasets.
4-7 years of experience in consulting or client-facing roles with data engineering and analytics.
Bachelor's or Master's degree in IT, Computer Science, Mathematics, Physics, or Statistics.
Hands-on experience (2-3 years) with Azure cloud technologies including Azure Databricks, Spark, Blob Storage, Virtual Machine, Functions, SQL Data Warehouse.
Proficiency in data warehousing, Azure Data Engineering concepts (data integration, quality, CI/CD pipelines, ADLS), and advanced skills in SQL, Python, and BI tools (Tableau, PowerBI).
Experienced in building and operating cloud-native data engineering solutions with Azure and modern data architectures (Snowflake, Databricks).
Consulting or client-facing experience with ability to translate business requirements into technical analytics and reporting deliverables.
Strong technical expertise in data modeling, visualization, and large-scale data manipulation to support machine learning models and advanced BI applications.