Data Engineer (Mid Level)-Orbit

About Irth Solutions

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)

Location

Remote – India

Department

Insights (AI/ML)

Reports to

Data Platform & Analytics Manager

About the Role

Irth is building a modern, multi-cloud, enterprise-grade data estate—a unified Databricks-based data platform that centralizes data across Irth’s products and cloud environments, including AWS, Azure, and GCP.

As a Data Engineer, you will play a hands-on implementation role, working closely with the Senior Data Architect to bring the enterprise data platform vision to life.

You will design and develop data pipelines based on established architectural patterns, implement data quality and governance controls, build Delta Lake and medallion architecture solutions, and help operationalize the new data platform.

This is an excellent opportunity for a mid-level Data Engineer looking to deepen their expertise in Databricks, Apache Spark, cloud data engineering, and modern lakehouse architecture while working in a multi-cloud enterprise environment.

Key Responsibilities

• Data Pipeline Development – Primary Responsibility

Build, maintain, and enhance data ingestion pipelines across AWS, Azure, and GCP, following architecture and engineering patterns established by the Senior Data Architect.

Develop both batch and streaming pipelines using

Databricks Workflows

Apache Spark / PySpark

SQL

Delta Live Tables

Databricks Lakeflow components

Implement Bronze → Silver → Gold medallion architecture patterns for ingestion, transformation, cleansing, and standardization.

Implement Change Data Capture (CDC) and Slowly Changing Dimensions (SCD Type 1 and Type 2).

Handle schema evolution and changing source-system structures.

Implement data validation, reconciliation, and quality rules as part of pipeline processing.

Build reusable and maintainable pipeline components following established engineering standards.

Platform & Storage Implementation

Configure and maintain Delta Lake storage structures, tables, schemas, partitions, and optimization routines.

Apply Delta Lake performance and maintenance practices, including

OPTIMIZE

Z-ORDER

VACUUM

Appropriate partitioning and file-management strategies

Assist with implementation of metadata, cataloging, and lineage standards using Unity Catalog.

Support integration between cloud storage platforms and Databricks, including

Amazon S3 → Databricks

Azure Storage → Databricks

Google Cloud Storage → Databricks

Assist with implementation of scalable storage and processing patterns defined by the Data Architect.

Data Governance, Quality & Compliance Enablement

Implement automated data-quality checks, profiling, validation, and monitoring in accordance with enterprise governance standards.

Apply data-quality rules at appropriate stages of the Bronze, Silver, and Gold layers.

Implement RBAC policies, security controls, and data-classification tags defined by the enterprise governance model.

Support implementation of metadata and lineage mapping across Unity Catalog and Microsoft Purview.

Help ensure datasets are properly documented, classified, governed, and discoverable.

Support remediation of data-quality and governance issues identified through monitoring or reviews.

Orchestration, Automation & Operational Support

Build, schedule, monitor, and maintain production workflows using

Databricks Workflows

Delta Live Tables

Azure Data Factory (ADF)

Other approved orchestration tools

Contribute to CI/CD pipelines for data-engineering code, including source control, automated testing, deployment, and environment management.

Support DEV → QA → PROD promotion processes.

Monitor production pipelines and respond to failures and data-quality issues.

Troubleshoot failed jobs, investigate root causes, and support pipeline recovery.

Perform performance tuning across Spark jobs, SQL workloads, Delta tables, and data pipelines.

Participate in operational improvements that increase pipeline reliability, scalability, and cost efficiency.

Collaboration & Documentation

Work directly with the Senior Data Architect to translate architecture designs and technical standards into actionable implementation tasks.

Participate in architecture reviews, technical design discussions, coding reviews, and engineering standards meetings.

Collaborate with Data Scientists, ML Engineers, Analysts, Product teams, and other engineering stakeholders to understand data requirements.

Document

Data pipelines

Data flows

Data dictionaries

Transformation logic

Data-quality rules

Test cases

Job schedules

Operational procedures

Maintain clear and accurate technical documentation to support platform adoption, troubleshooting, and future development.

Provide implementation feedback to the Data Architect and identify opportunities to improve platform patterns, tooling, and developer experience.

Role Scope

This is primarily an implementation-focused Data Engineering role. The Senior Data Architect will establish the overall platform architecture, standards, and design patterns; the Data Engineer will translate those patterns into reliable, production-ready pipelines and platform capabilities.

The role provides an opportunity to gain deeper hands-on experience with Databricks, Spark, Delta Lake, Unity Catalog, cloud data platforms, data governance, and multi-cloud lakehouse engineering while contributing to a strategic enterprise data platform.

Requirements

Qualifications

Required Qualifications

3–5 years of experience in Data Engineering, ETL developme