Data Engineer – Mid Level

🔥 0 minutes ago

🇺🇸 United States – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

🚰 Data Engineer

👻 Ghost score 16%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Irth Solutions

WebsiteLinkedInAll Job Openings

51 - 200 employees

Founded 1995

☁️ SaaS

⚡ Energy

📡 Telecommunications

💰 Private Equity Round on 2015-07

SaaS • Energy • Telecommunications

Irth Solutions is a market-leading provider of a SaaS platform focused on enhancing resilience and reducing risk in the management of critical network infrastructure. Their solutions are trusted by energy, utility, and telecom companies across the U. S. and Canada, offering capabilities in damage prevention, training, asset inspections, land management, and 811 ticket management. By leveraging business intelligence, analytics, and geospatial data, Irth Solutions provides comprehensive situational awareness to proactively manage and mitigate risks in network infrastructure. The acquisition of OneBridge Solutions enhances their asset performance management offerings.

📋 Description

• Build and maintain ingestion pipelines for structured and semi-structured sources, including GIS, inline inspection data, SCADA, maintenance systems, and enterprise systems of record. • Implement batch and streaming ingestion using Databricks Workflows, Spark, PySpark, SQL, and declarative pipeline tooling. • Apply Bronze, Silver, and Gold medallion architecture patterns for transformation, standardization, and enrichment. • Implement change data capture, slowly changing dimensions, schema evolution, and data-validation rules. • Normalize third-party and public data feeds, including weather history, soil characteristics, satellite-derived data, and one-call ticket data. • Build AI-assisted pipelines for normalization of units, schemas, and semantics across inconsistent customer data. • Implement automated data-quality repair workflows with provenance for synthesized values. • Productionize document-extraction pipelines with data scientists. • Build human-in-the-loop review and exception workflows for low-confidence extractions. • Configure and manage Delta Lake tables, partitioning strategies, and optimization routines. • Implement metadata, lineage, and cataloging standards using Unity Catalog. • Build and maintain connectors to customer systems of record with configurable refresh cadences. • Support geospatial data processing, including spatial joins and alignment to pipeline centerline geometry. • Implement data-quality tests, profiling, drift monitoring, access controls, security rules, classification tags, and regulatory traceability. • Build, schedule, and monitor workflows; own alerting and pipeline failure handling. • Contribute to CI/CD for pipeline code, including version control, automated testing, and environment promotion. • Troubleshoot production incidents, recover failed pipeline runs, and optimize performance and infrastructure costs. • Translate Data Architect designs into production implementations and identify design gaps or ambiguities. • Participate in architecture, design, and code reviews. • Document pipelines, transformation logic, data dictionaries, job schedules, operational procedures, and runbooks. • Deliver reliable Bronze, Silver, and Gold data flows, reduce manual customer-data onboarding effort, maintain high data-quality pass rates, comply with governance standards, minimize incidents, and collaborate with architects, data scientists, application engineers, and cross-functional partners.

🎯 Requirements

• 3–5 years of experience in data engineering, ETL development, or cloud data platform engineering. • Hands-on experience with Databricks, Spark, PySpark, or comparable distributed data-processing technologies. • Strong SQL skills and experience with structured data transformation. • Experience with at least one major cloud platform; Azure experience preferred. • Familiarity with data modeling, data-quality practices, schema evolution, and pipeline troubleshooting. • Experience with workflow orchestration and scheduling frameworks. • Understanding of core data-security practices, including access control, encryption, and credential management. • Experience with Git-based development and comfort working within a code-reviewed engineering team. • Experience with Delta Lake, medallion architecture, and lakehouse engineering best practices preferred. • Experience with Unity Catalog, Microsoft Purview, or comparable metadata and data-lineage tooling preferred. • Experience building pipelines that ingest unstructured or semi-structured documents preferred. • Experience with geospatial data processing and common GIS data formats preferred. • CI/CD and DevOps experience for data workloads, including infrastructure as code (IaC), preferred. • Experience preparing and transforming data specifically for machine learning or probabilistic model consumption preferred. • Cloud or Databricks certifications preferred. • Experience using AI-assisted coding tools such as Cursor or GitHub Copilot and/or agentic coding tools such as Claude Code as part of a professional development workflow preferred. • Experience integrating oil and gas or utility asset data, including pipelines, facilities, and GIS assets, into a data platform nice to have. • Understanding of asset integrity concepts, including inspection data, risk scoring, corrosion, and defect tracking, nice to have. • Familiarity with regulatory and compliance reporting requirements for pipeline or asset integrity data, nice to have. • Experience migrating customers from legacy or spreadsheet-based systems to modern data platforms, nice to have.