Free · takes a minute · your resume fills your profile
CoreHRx never asks candidates for money. If anyone does, .
Role at a glance
You will
Design and implement Unity Catalog strategies to manage data access, lineage, and governance effectively.
Architect and maintain Delta Lake structures to ensure high-performance data storage and processing.
Conduct comprehensive data profiling and establish rigorous validation frameworks to uphold data integrity.
Experience
5+ years
Must-have skills
Data EngineeringDatabricksGitMachine LearningPythonQuality Assurance
How well do your skills match this job?
Sign in to see your fit: the skills, experience and location this job asks for, checked against your profile.
Job description
Summary We are seeking a highly skilled Semantic & Genie Engineer to join our IT Services team, focusing on advanced data architecture and intelligent automation within the Databricks ecosystem. This pivotal role is designed to leverage cutting-edge technologies to enhance data governance, optimize query performance, and develop sophisticated multi-agent orchestration solutions. The ideal candidate will bridge the gap between complex data engineering and generative AI capabilities, ensuring robust data quality and seamless integration of semantic layers across our platforms.
Responsibilities
Design and implement Unity Catalog strategies to manage data access, lineage, and governance effectively.
Architect and maintain Delta Lake structures to ensure high-performance data storage and processing.
Conduct comprehensive data profiling and establish rigorous validation frameworks to uphold data integrity.
Optimize complex SQL queries and database performance to support real-time analytics and reporting needs.
Develop and deploy custom applications using Python, Streamlit, and Databricks Apps to visualize insights and automate workflows.
Engineer advanced multi-agent systems and Databricks Genie Agents to drive autonomous data operations and decision-making.
Configure and monitor Databricks Metric Views to track key performance indicators and system health.
Manage version control and collaborative development processes using Git for all codebases and configurations.
Integrate Machine Learning Pipelines (MCP) to enhance the predictive capabilities of data solutions.
RequirementsRequirements:
Minimum of 5 years of professional experience in data engineering or related technical fields.
Proficiency in Python programming and advanced SQL for data manipulation and analysis.
Deep expertise with Databricks ecosystem components, including Unity Catalog, Delta Lake, and Databricks Genie.
Strong background in data quality assurance, profiling techniques, and validation methodologies.
Demonstrated ability to perform query optimization and tune data pipelines for maximum efficiency.
Experience building interactive dashboards and applications using Streamlit and Databricks Apps.
Knowledge of multi-agent orchestration patterns and the implementation of Genie Agents/Spaces.
Familiarity with Git version control systems and collaborative development practices.
Understanding of Machine Learning Pipelines (MCP) and their integration into data workflows.
Key skills
Required
Data EngineeringDatabricksGitMachine LearningPythonQuality AssuranceQuery OptimizationSQL
This page helps Indian job seekers prepare for Python interviews for backend, data engineering and data analyst roles, whether you are a fresher facing your first technical round or an experienced developer aiming for a senior position. It has 60 questions in three levels, easy, medium and hard, each with a concise sample answer and, where it helps, runnable Python 3.12 code. Topics include data structures, OOP, generators, decorators, the GIL and concurrency, type hints, testing and performance. Answer each question aloud in your own words first, then compare with the sample answer and run the code to check your understanding.
Questions you may be asked
What is the difference between mutable and immutable types in Python?
What is the difference between a list and a tuple?
