Description:

Own Databricks production support for the company's data platform, including monitoring, alerting, and incident response.
Maintain and report on SLA performance metrics for data pipeline delivery.
Identify and implement pipeline optimizations to reduce costs and improve throughput.
Migrate legacy ETL/ELT pipelines to Databricks and build automation tooling.
Support new customer onboarding by provisioning and validating tenant data pipelines.
Design and build high-performance Databricks pipelines for ERP and CRM data.
Own Delta Lake architecture, including schema design and data quality enforcement.
Enforce data security best practices across Databricks environments.
Implement data quality monitoring and observability across pipeline health.
Apply multi-tenant data isolation patterns for secure data delivery.
Partner with Enterprise Architecture for seamless data pipeline integration.
Support global operations through on-call rotation and incident response.
Maintain technical documentation and contribute to team knowledge sharing.
Apply CI/CD best practices to data pipeline development.

Requirements:

4+ years of data engineering experience.
At least 2 years of experience with Databricks or Apache Spark across Azure and/or AWS.
Proficiency in PySpark, SQL, and Python with experience in production-grade pipelines.
Hands-on experience with Delta Lake and pipeline performance tuning.
Solid knowledge of PostgreSQL and legacy ETL tooling support.
Experience with large-scale multi-tenant architectures and data privacy.
Proven ability to collaborate across data science, product, and infrastructure teams.
Strong understanding of data governance, security, and compliance principles.

Opportunity to work with a global technology company specializing in innovative solutions.
Engage in complex technical challenges and high-quality data platform development.

Skills