Data Engineer

Intetics Inc. is a global technology company providing custom software application development, distributed professional teams, software product quality assessment, and all-things-digital solutions.

• 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.

• 4+ Years of data engineering experience;
• At least 2 years on Databricks or the Apache Spark ecosystem across Azure and/or AWS;
• Proficiency in PySpark, SQL, and Python with a strong track record building and operating production-grade pipelines under SLA constraints;
• Hands-on experience with Delta Lake including schema evolution, ACID transactions, optimize/vacuum lifecycle, and both incremental and streaming processing patterns;
• Hands-on experience with pipeline performance tuning and compute optimization in production Databricks environments;
• Solid working knowledge of PostgreSQL including query optimization, schema design, and use as a source or sink in production data pipelines;
• Experience supporting and maintaining legacy ETL tooling in production;
• Experience supporting large-scale multi-tenant architectures with a focus on tenant isolation, per-tenant performance, and data privacy;
• Proven ability to work collaboratively across Data Science, Product, and Infrastructure teams, owning end-to-end delivery in a cross-functional environment;
• Strong understanding of data governance, security, and compliance principles;
• Nice to have: Experience operating Databricks workspaces across both Azure and AWS, Experience optimizing Databricks workloads in a Serverless environment, Experience with Microsoft SQL Server in a data engineering or ETL context, Exposure to ML feature engineering or feature stores supporting predictive analytics, Experience with customer onboarding automation or IaC patterns for provisioning tenant data pipelines at scale, Databricks Certified Data Engineer Associate or Professional certification.

• No conditions specified