Role Overview

We are seeking a hands-on Data Engineer to join our team, focusing on building the

robust foundations required for modern analytics and AI. In this role, you will bridge

the gap between raw data and production-ready AI solutions, architecting scalable

pipelines that power machine learning models and business intelligence. You will be

responsible for the engineering discipline that ensures data reliability, performance, and

security within the Databricks ecosystem.

Key Responsibilities

• AI/ML Pipeline Support: Architect, build, and maintain scalable data pipelines

specifically designed to support AI/ML workflows, including feature

engineering and model training datasets.

• Design and implement robust data pipelines, data lakes, and data warehousing

solutions.

• Partner with data scientists and analysts to deploy machine learning models and

analytics solutions on Databricks.

• Enhance Databricks cluster performance, ensuring cost-effectiveness and efficient

resource utilization.

• Implement Databricks security features, enforce data governance, and maintain

compliance with industry standards.

• MLOps Integration: Collaborate with Data Scientists to deploy and

operationalize machine learning models into production environments securely.

• Contribute to the technical direction and best practices of data engineering

initiatives.

• Stay current with Databricks innovations, best practices, and industry trends,

and share knowledge across the team.Required Skills & Qualifications

• Total Experience: 1-4 years in data engineering or related fields.
• Databricks Expertise: Solid experience with Databricks, focusing on platform

engineering, data engineering, and analytics.

• Technical Skills: Proficiency in Python, Scala, or Java; strong experience with

Databricks APIs, Apache Spark, and Delta Lake.

• Data Engineering Background: Solid experience in data warehousing, ETL

processes, and data governance frameworks.

• AI/ML Exposure: Practical experience supporting AI/ML lifecycles, including

familiarity with ML frameworks (e.g., TensorFlow, PyTorch) or MLOps practices.

• Collaboration & Ownership: Proven ability to work independently, take

ownership of projects, and collaborate effectively with a team.

• Communication: Excellent interpersonal and communication skills for effective

collaboration with stakeholders.Good to Have

• Cloud Experience: Hands-on experience with AWS, Azure, or GCP.
• Certifications: Databricks Certified Professional or equivalent.
• Machine Learning: Familiarity with ML concepts and popular libraries (e.g.,

TensorFlow, PyTorch, Scikit-learn).What we are offering

• Best-in-industry learning and growth opportunities.
• Comprehensive Health and wellness benefits, including medical insurance for

self and family.

• Employee rewards program and paid parental leave.
• Flexible schedule and remote working benefits.