Role Summary
You'll build and maintain systems for efficient data collection, storage, and processing to ensure data pipelines are robust and scalable for seamless integration and analysis. We are seeking an individual who not only possesses the requisite expertise but also thrives in the dynamic landscape of a fast-paced global firm.
What You'll Do
- Design/implement complex and scalable enterprise data processing and BI reporting solutions.
- Design, build and optimize ETL pipelines or underlying code to enhance data warehouse systems
- Work towards optimizing the overall costs incurred due to system infrastructure, operations, change management etc.
- Deliver end-to-end data solutions across multiple infrastructures and applications
- Coach, mentor, and manage a team of junior associates to help them(plan tasks effectively and more)
- Demonstrate overall client stakeholder and project management skills (drive client meetings, creating realistic project timelines, planning and managing individual and team's task)
- Assist senior leadership in business development proposals focused on technology by providing SME support
Must Have
- 3 5 years of experience in designing/building data warehouses and BI reporting with a B.Tech/B.E background
- Prior experience of managing client stakeholders and junior team members.
- A background in managing Life Science clients is mandatory
- Proficient in big data processing and cloud technologies like AWS, Azure, Databricks, PySpark, Hadoop etc. Along with proficiency in Informatica is a plus.
- Extensive hands-on experience in working with cloud data warehouses like Redshift, Azure, Snowflake etc. And Proficiency in SQL, Data modelling, designing ETL pipelines is a must.
- Intermediate to expert level proficiency in Python, Proficiency in either Tableau, PowerBI, Qlik is a must.
- Should have worked on large datasets and complex data modelling projects.
- Domain knowledge of the pharma landscape is a must have.
Skills: bi reporting,data collection,python,databricks,powerbi,tableau,azure,sql,redshift,designing/building data warehouses,pharma landscape,hadoop,data processing,aws/azure,pyspark,aws,informatica,cloud technologies,qlik,data pipelines,data modelling,enterprise data processing,data storage,snowflake,etl pipelines,visualization
