Senior Data Engineer – Databricks

🔥 0 minutes ago

🌐 Argentina, Colombia, +4 more countries – Remote

🇦🇷 Argentina – Remote 🇨🇴 Colombia – Remote 🇧🇷 Brazil – Remote 🇪🇨 Ecuador – Remote 🇵🇪 Peru – Remote 🇨🇷 Costa Rica – Remote

⏰ Full Time

🟠 Senior

🚰 Data Engineer

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Intetics

WebsiteLinkedInAll Job Openings

501 - 1000 employees

Founded 1995

💼 Consulting

🏥 Healthcare

📦 Logistics

Consulting • Healthcare • Logistics

Intetics is an innovative company providing custom software development services, specializing in AI and machine learning solutions. They offer Remote In-Sourcing® to build expert teams for software engineering and data processing projects, along with advanced tools like TETRA™ for software quality assessment. Intetics aims to empower businesses by leveraging high-quality data and integrating modern technologies across various industries, including healthcare, finance, and more.

📋 Description

• 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.

🎯 Requirements

• 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 (SSIS, Informatica, custom Python/SQL pipelines, or similar) in production. • 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. • 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, including access control, data privacy, and protection of sensitive enterprise data across multi-tenant environments.