Data Engineering & Operations Lead

Shelter Mutual Insurance Company•Columbia, MO

5d•Onsite

About The Position

This role is responsible for the architecture, design, implementation, and maintenance of data warehouse operations. Expertise in Snowflake, AWS, and Qlik tools is required to ensure efficient and scalable data storage, processing, and analytics. The company offers a broad salary range to account for variations in location, education, training, skills, experience, business needs, and market demands. The stated range is base pay only and does not include other components of the total rewards package.

Requirements

7+ years of experience as a Data Engineering Practitioner.
Proficiency in Qlik Replicate, Compose, DBTLabs & Astronomer for data integration, replication, and transformation.
Strong understanding of data engineering principles, data modeling, ETL processes, and data warehousing concepts.
Expertise in Snowflake, including data ingestion, storage, performance optimization, and security.
Experience with cloud platforms, such as AWS or Azure, and working with cloud-based data solutions and services.
Familiarity with data quality, data governance, and data lifecycle management practices.
Solid programming skills in SQL and scripting languages (e.g., Python, PowerShell) for data manipulation and automation.
Strong problem-solving and troubleshooting abilities.
Excellent communication and collaboration skills.
Ability to perform the essential functions of the position, with or without a reasonable accommodation.

Nice To Haves

Bachelor's or Master's degree in computer science, information systems, or a related field.
Knowledge of dbtLabs, Astronomer, Github.

Responsibilities

Architecture & design, implementation, and maintenance of data warehouse operations.
Ensure efficient and scalable data storage, processing, and analytics using Snowflake, AWS, and Qlik tools.
Data ingestion, storage, performance optimization, and security in Snowflake.
Working with cloud-based data solutions and services on cloud platforms like AWS or Azure.
Data manipulation and automation using SQL and scripting languages (e.g., Python, PowerShell).
Diagnose and resolve data engineering issues.
Work effectively with cross-functional teams and stakeholders.