Data Engineer (Mid Level)-Orbit

Irth Solutions is a leading provider of cloud-based SaaS software for damage prevention, asset integrity, stakeholder engagement and land management, helping energy, utility, telecom, and infrastructure companies protect their critical network infrastructure. With nearly three decades of industry experience, Irth serves customers across North America and continues to expand its platform with new data-driven and AI-powered capabilities.

Data Engineer – Insights (AI/ML)

Qualifications

Required Qualifications

• 3–5 years of experience in Data Engineering, ETL development, or cloud data platform engineering.
• Hands-on experience with Databricks, Apache Spark, PySpark, or other distributed data-processing technologies.
• Strong proficiency in SQL, including structured data transformation, joins, aggregations, and performance-aware query development.
• Experience working with at least one major cloud platform, with Microsoft Azure preferred; AWS and/or GCP experience is also valuable.
• Understanding of core data-engineering concepts, including:
• Data modeling
• Data quality
• Schema evolution
• Data validation
• Pipeline monitoring and troubleshooting
• Basic understanding of data-security practices, including:
• Role-Based Access Control (RBAC)
• Encryption
• Credential and secret management
• Secure access to cloud and data-platform resources

Preferred Qualifications

• Hands-on or working knowledge of Delta Lake, medallion architecture, and modern lakehouse best practices.
• Experience with metadata, cataloging, and governance platforms such as:
• Unity Catalog
• Microsoft Purview
• AWS Glue Data Catalog
• Similar enterprise metadata and data-governance tools
• Experience with workflow orchestration and scheduling technologies, such as:
• Azure Data Factory (ADF)
• Databricks Workflows
• Apache Airflow
• Databricks Jobs / DBX
• Similar orchestration frameworks
• Experience with Git-based development, CI/CD, and DevOps practices.
• Knowledge or experience in one or more of the following areas:
• Geospatial/GIS data
• BI semantic layers, particularly Power BI
• Data preparation for AI/ML workloads
• Relevant cloud or Databricks certifications, such as:
• Databricks Data Engineer Associate
• Microsoft Azure Data Engineer Associate (DP-203)
• Equivalent cloud or data-engineering certifications

Nice-to-Have Qualifications

• Understanding of asset integrity management concepts, including inspection data, risk scoring, corrosion tracking, defect management, and maintenance data as applied to pipeline or utility operations.
• Previous experience working with or integrating oil & gas, utility, infrastructure, or pipeline asset data into enterprise data platforms.
• Experience working with:
• Pipeline and facility data
• GIS/geospatial asset data
• Inspection and maintenance records
• Asset-risk datasets
• Familiarity with regulatory, compliance, and audit-reporting requirements associated with pipeline, utility, or asset-integrity data.

Success Metrics

Success in this role will be measured by the engineer’s ability to reliably implement and operationalize the data-platform patterns established by the Data Architect.

Key measures include:

• High-quality implementation of ingestion, transformation, data-quality, and governance patterns defined by the Data Architect.
• Reliable and maintainable pipelines supporting consistent Bronze → Silver → Gold data flows.
• Strong adherence to cataloging, metadata, lineage, security, and data-governance standards.
• Reduction in pipeline failures and production incidents through improved monitoring, testing, troubleshooting, and operational practices.
• Continuous improvements in pipeline performance, scalability, reliability, and maintainability.
• Clear and complete technical documentation covering pipelines, transformations, data-quality rules, and operational procedures.
• Effective collaboration with the Senior Data Architect, engineering teams, product teams, and business stakeholders.
• Demonstrated ability to take architecture guidance and translate it into production-ready, scalable data-engineering solutions.