Intetics Inc. is a global technology company specializing in custom software development, AI-powered solutions, cloud technologies, and digital transformation. With over 30 years of experience, we help organizations worldwide build scalable, innovative, and data-driven solutions across a wide range of industries. We are looking for talented professionals who are passionate about solving complex technical challenges and building high-quality data platforms.
Impact You Will Make in the Role:
Responsibilities
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Own Databricks production support for the company’s data platform, including monitoring, alerting, and incident response across all production data flows.
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Maintain and report on SLA performance metrics for data pipeline delivery, ensuring visibility into platform health and accountability across internal and external stakeholders.
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Identify and implement pipeline optimizations that reduce Databricks compute costs, improve throughput, and reduce processing windows while tracking impacts through measurable KPIs.
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Migrate legacy ETL/ELT pipelines to Databricks, building automation tooling to reduce manual intervention and ensure uninterrupted data delivery during transitions.
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Support new customer onboarding by provisioning, validating, and hardening tenant data pipelines that deliver reliable, isolated data from day one.
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Design and build high-performance Databricks pipelines that ingest, transform, and serve ERP and CRM data at scale across both Azure and AWS environments.
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Own the Delta Lake architecture, including schema design, partitioning strategies, data quality enforcement, and incremental processing patterns.
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Enforce data security best practices across Databricks environments, including role-based access control, secrets management, and compliance requirements for enterprise business data.
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Implement data quality monitoring and observability across pipeline health and ML model inputs, ensuring data integrity that directly supports predictive analytics.
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Apply and enforce multi-tenant data isolation patterns, ensuring reliable and secure data delivery across enterprise customers.
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Partner with the Enterprise Architecture team to ensure data pipelines integrate seamlessly with the broader AI and analytics ecosystem.
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Support a globally distributed operation through on-call rotation and after-hours incident response, meeting SLAs across multiple time zones.
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Maintain technical documentation, runbooks, and architectural decision records, contributing to team knowledge sharing and operational readiness across on-call and incident response scenarios.
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Apply CI/CD best practices to data pipeline development, including version control, automated testing, and deployment tooling to ensure reliable and repeatable pipeline delivery.
Requirements
What You Will Bring:
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4+ years of data engineering experience.
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At least 2 years of experience with Databricks or the Apache Spark ecosystem across Azure and/or AWS.
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Proficiency in PySpark, SQL, and Python with a strong track record of building and operating production-grade pipelines under SLA constraints.
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Hands-on experience with Delta Lake, including schema evolution, ACID transactions, optimize/vacuum lifecycle, and both incremental and streaming processing patterns.
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Hands-on experience with pipeline performance tuning and compute optimization in production Databricks environments.
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Solid working knowledge of PostgreSQL, including query optimization, schema design, and use as a source or sink in production data pipelines.
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Experience supporting and maintaining legacy ETL tooling (SSIS, Informatica, custom Python/SQL pipelines, or similar) in production.
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Experience supporting large-scale multi-tenant architectures with a focus on tenant isolation, per-tenant performance, and data privacy, including navigating tools and platforms that default to single-tenant assumptions.
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Proven ability to work collaboratively across data science, product, and infrastructure teams, owning end-to-end delivery in a cross-functional environment.
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Strong understanding of data governance, security, and compliance principles, including access control, data privacy, and protection of sensitive enterprise data across multi-tenant environments.
Preferred Qualifications / Experience:
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Experience operating Databricks workspaces across both Azure and AWS, including cost governance, cluster management, and cross-cloud data access.
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Experience optimizing Databricks workloads in a Serverless environment, including compute cost governance and performance tuning for serverless compute.
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Experience with Microsoft SQL Server in a data engineering or ETL context.
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Exposure to ML feature engineering or feature stores (Databricks Feature Store, Feast, or similar) supporting predictive analytics.
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Experience with customer onboarding automation or Infrastructure as Code (IaC) patterns for provisioning tenant data pipelines at scale.
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Databricks Certified Data Engineer Associate or Professional certification.