Role: Senior Data Engineer

Function: Data Engineering

Industry: Mining services, Data analytics

About Company

You join an established provider of diversified drilling services that partners with global mining and exploration clients. The team pairs decades of field expertise with a new data-driven initiative to reinvent drilling operations for the 21st century. Backed by steady revenue and leadership committed to digital transformation, the company invests heavily in analytics, safety, and operational efficiency. You will help turn field data into actionable insights that reduce downtime and boost profitability across active drill sites.

Position Overview

You design and maintain the data pipelines that power the client’s flagship digital initiative to modernize drilling operations. You work alongside commercial leaders to surface clean, reliable datasets that drive safer, more profitable decisions. Your work sets the technical foundation for analytics used by internal teams and external stakeholders.

Role & Responsibilities

• Design, develop, and maintain data pipelines for efficient and reliable extraction, transformation, and loading.

• Perform data manipulation and transformation to keep datasets cleansed, structured, and optimized for analysis.

• Create and update documentation covering pipeline logic, data definitions, lineage, and metadata.

• Monitor and enforce data integrity and quality across the entire pipeline.

• Implement secure data-sharing mechanisms with clients and other external stakeholders while meeting privacy regulations.

• Educate and train stakeholders on data practices, governance, and quality assurance.

• Build and maintain high-quality dimensional models for downstream analytics.

• Review pull requests from contributors to uphold quality standards and best practices.

Must have Criteria

• 3–4 years of experience as a data engineer or in a related role.

• Proficiency with Microsoft Azure, including Azure Data Factory and Azure Functions.

• Hands-on experience with Snowflake and strong knowledge of data-warehousing concepts such as modeling, schema design, and performance optimization.

• Strong SQL skills for data manipulation, querying, and tuning.

• Proficient in Python programming.

• Advanced knowledge of dbt.

• Ability to integrate data from various sources using RESTful APIs or other methods.

• Experience designing and implementing ETL and ELT data pipelines.

• Solid understanding of dimensional modeling and Kimball principles.

• Familiarity with data-validation techniques to ensure accuracy and quality.

• Strong problem-solving and communication skills.