Lead Data Analyst / Data Engineer - mosaic.ai
Key Responsibilities
• Lead the design and development of scalable ETL pipelines and data architectures
• Build and optimize data workflows using Databricks and Python
• Manage large-scale structured and unstructured datasets
• Drive data modeling, transformation, and data quality initiatives
• Develop advanced analytics and predictive models (where required)
• Collaborate with business teams to translate requirements into data solutions
• Build dashboards and reporting frameworks for business insights
• Ensure data governance, security, and best practices across systems
• Mentor and guide junior team members
Required Skills & Expertise
Core Technical Skills
• Strong experience in Python programming
• Deep expertise in ETL development and data pipeline design
• Hands-on experience with Databricks (must-have)
• Strong SQL skills and experience with relational databases
• Experience with data warehousing concepts and architectures
Data Engineering & Processing
• Experience with big data technologies (Spark, PySpark)
• Building scalable and optimized data pipelines
• Experience with batch and real-time data processing
• Lead the design and development of scalable ETL pipelines and data architectures
• Build and optimize data workflows using Databricks and Python
• Manage large-scale structured and unstructured datasets
• Drive data modeling, transformation, and data quality initiatives
• Develop advanced analytics and predictive models (where required)
• Collaborate with business teams to translate requirements into data solutions
• Build dashboards and reporting frameworks for business insights
• Ensure data governance, security, and best practices across systems
• Mentor and guide junior team members
Required Skills & Expertise
Core Technical Skills
• Strong experience in Python programming
• Deep expertise in ETL development and data pipeline design
• Hands-on experience with Databricks (must-have)
• Strong SQL skills and experience with relational databases
• Experience with data warehousing concepts and architectures
Data Engineering & Processing
• Experience with big data technologies (Spark, PySpark)
• Building scalable and optimized data pipelines
• Experience with batch and real-time data processing