Data Engineer II

About The Opportunity

Netrix Global is seeking a Data Engineer II with strong technical fundamentals and a growth mindset to join our team in Pune, India. This role is ideal for an engineer eager to build expertise in data engineering and enterprise platform integration, working at the intersection of data pipelines and operational systems.

You will support internal analytics infrastructure and key business applications, designing and maintaining ETL pipelines, supporting reporting infrastructure, and assisting with the administration of platforms like Kimble (Kantata), Salesforce Lightning, and Quickbase. Prior deep expertise in these specific platforms is not required; we value technical curiosity, resourcefulness, and a motivation to grow into system ownership.

How You Will Make An Impact

• Design, build, and maintain data ingestion, transformation, and storage pipelines utilizing Azure services (Azure Data Factory, Azure Synapse Analytics, Azure Data Lake).
• Support the development and optimization of ETL/ELT workflows for structured and unstructured data.
• Monitor, troubleshoot, and optimize data pipelines and cross-system integrations for performance and reliability.
• Collaborate with business teams to design and maintain Power BI dashboards and datasets, ensuring data accuracy and visualization best practices.
• Implement data validation, quality checks, and document data processes.
• Assist with administration and configuration support for Kimble (Kantata), Salesforce Lightning, and Quickbase to ensure reliable operational workflows.
• Support cross-platform data synchronization and reporting consistency.
• Contribute to data governance, security, and compliance practices within Azure environments.
• Continuously identify opportunities to automate processes and enhance system efficiency.
• Stay updated with Azure, BI, and platform technologies, recommending proactive improvements.

What You Will Bring To The Table

We seek an engineer who operates with ownership, curiosity, and accountability. Success involves recognizing gaps, identifying improvement opportunities, and proactively driving work forward.

• Demonstrate strong self-direction and resourcefulness, leveraging documentation, experimentation, and collaboration to solve problems independently.
• Take ownership of systems and outcomes, ensuring work is completed thoroughly.
• Adapt quickly to evolving technologies, tools, and responsibilities with a focus on continuous skill expansion.
• Collaborate effectively across technical and non-technical teams, sharing knowledge and fostering a supportive engineering culture.
• Balance independence with clear communication, escalating risks early and keeping stakeholders informed.
• Thrive in an environment where priorities evolve and engineers are empowered to shape solutions.

Qualifications

• 1-3 years of experience in data engineering, analytics engineering, or systems support, or equivalent demonstrated technical capability.
• Bachelor's degree in Computer Science, Information Technology, or a related field is preferred.
• Strong working knowledge of SQL and relational database concepts.
• Hands-on experience with ETL processes, data transformation, or pipeline development.
• Working familiarity with Azure data services such as Azure Data Factory, Azure Synapse Analytics, and Azure Data Lake is preferred.
• Exposure to Power BI for reporting and dataset development.
• Familiarity with Python or PySpark for data manipulation is a plus.
• Understanding of data modeling, warehousing, and ETL concepts.
• Demonstrated ability to troubleshoot unfamiliar systems and learn new platforms quickly.
• Exposure to enterprise SaaS platforms like Salesforce or Quickbase is a plus.
• Strong analytical, documentation, and communication skills.

Work Schedule

This role is based in Pune, India, and supports US-based teams. To ensure real-time collaboration, the engineer must maintain consistent daily overlap with US Central Time. The working schedule will align with Indian evening hours, extending until 11:00 AM US Central Time. This is a late shift, designed for predictable hours and strong coverage for US operations. Exact start times may be adjusted slightly for work-life balance while maintaining required coverage.