Intetics Inc.

Intetics Inc., a global technology company providing custom software application development, distributed professional teams, software product quality assessment, and “all-things-digital” solutions, is seeking a highly skilled and experienced Senior Data Engineer to join our dynamic team on a full-time basis.

About the Project

We are redefining CRM for the age of AI.

We’re delivering on the original promise of CRM - turning fragmented customer and revenue signals into clear, prioritized action. Instead of more dashboards or surface-level insights, we help teams focus on what matters most and know exactly what to do next.

More than two decades after our founding, we’re entering a new chapter with clarity and momentum - building intelligent, intuitive solutions that work within the flow of how teams actually sell and serve. We’re focused on solving complex, real-world challenges where relationships, context, and precision make all the difference.

Our global team is united by a shared commitment to impact, ownership, and continuous growth. We create an environment where thoughtful ideas move quickly, where people are trusted to lead, and where flexibility supports how great work gets done.

If you’re excited to help shape what’s next in AI-driven CRM - and build technology that drives real outcomes - we’d love to meet you.

Where You Fit In

The predictive data platform powers revenue intelligence for mid-market enterprises by fusing ERP and CRM data into actionable insights.

As a Senior Data Engineer, you will own the Databricks pipelines that make this possible, driving production reliability, cost efficiency, and platform growth through customer onboarding and legacy modernization.

You will work closely with ML engineers, product teams, and the Enterprise Architecture team to ensure the data backbone behind the predictive platform is always fast, clean, and ready to deliver at a global scale.

Impact You Will Make in the Role

• Own Databricks production support for the predictive data platform, including monitoring, alerting, and incident response across all production data flows.
• Maintain and report on SLA performance metrics for data pipeline delivery, ensuring visibility into platform health and accountability across internal and external stakeholders.
• Identify and implement pipeline optimizations that reduce Databricks compute costs, improve throughput, and reduce processing windows while tracking impacts through measurable KPIs.
• Migrate legacy ETL/ELT pipelines to Databricks, building automation tooling to reduce manual intervention and ensure uninterrupted data delivery during transitions.
• Support new customers onboarding by provisioning, validating, and hardening tenant data pipelines that deliver reliable, isolated data from day one.
• Design and build high-performance Databricks pipelines that ingest, transform, and serve ERP and CRM data at scale across both Azure and AWS environments.
• Own the Delta Lake architecture including schema design, partitioning strategies, data quality enforcement, and incremental processing patterns.
• Enforce data security best practices across Databricks environments, including role-based access control, secrets management, and compliance requirements for enterprise CRM and ERP data.
• Implement data quality monitoring and observability across pipeline health and ML model inputs, ensuring data integrity that directly supports model prediction accuracy.
• Apply and enforce multi-tenant data isolation patterns ensuring reliable, secure data delivery across enterprise customers.
• Partner with the Enterprise Architecture team to ensure data pipelines integrate seamlessly with the broader product ecosystem.
• Support a globally distributed operation through on-call rotation and after-hours incident response, meeting SLAs across multiple time zones.
• Maintain technical documentation, runbooks, and architectural decision records, contributing to team knowledge sharing and operational readiness across on-call and incident response scenarios.
• Apply CI/CD best practices to data pipeline development, including version control, automated testing, and deployment tooling to ensure reliable and repeatable pipeline delivery.