Description:

Design and deliver end-to-end data platforms for analytics, BI, machine learning, and AI-ready data products.
Build and optimize scalable ETL/ELT pipelines using Databricks, Spark/PySpark, SQL, and Python.
Own data processing logic, transformations, and platform performance for reliable, scalable solutions.
Design lakehouse architectures aligned with business needs.
Improve pipelines, development practices, and delivery processes.
Collaborate with architects, analysts, and stakeholders to create effective solutions.
Apply data quality, governance, and security standards across the platform.
Mentor engineers and share knowledge across the team.
Lead or contribute to technical design discussions and influence architecture decisions.

Requirements:

5+ years’ experience in data engineering or similar roles.
Strong hands-on experience with Spark/PySpark, SQL, Python, and Databricks or similar platforms.
Experience building ETL/ELT pipelines and data warehouse or lakehouse architectures.
Solid understanding of data modeling, warehousing, and batch or real-time processing.
Experience with cloud platforms (Azure or AWS) and modern data platforms.
Experience applying data governance, security, quality, lineage, and lifecycle practices.
Experience with orchestration, CI/CD, automated deployments, or Infrastructure as Code.
Experience building production-grade, observable, and scalable data pipelines.
Ability to work independently on complex, large-scale projects.
Strong communication skills across technical and business audiences.

Skills