Director - Data Engineering - Life Sciences (10-15 yrs)
- Assist team in conceptualizing, developing and delivering PoCs and new platforms
- Responsible for designing, deploying, and maintaining analytics environment that processes data at scale.
- Contribute design, configuration, deployment, and documentation for components that manage data ingestion, real time streaming, batch processing, data extraction, transformation, enrichment, and loading of data into a variety of cloud data platforms, including AWS and Microsoft Azure.
- Assist team in developing and delivering PoCs and platforms
- Evaluate new and upcoming analytics solutions and make recommendations for adoption to extend our platform to meet advanced analytics use cases, such as predictive modeling and recommendation engines.
- Data Modelling, Data Warehousing on Cloud Scale using Cloud native solutions.
- Perform development, QA, and dev-ops roles as needed to ensure total end to end responsibility of solutions
- Strong experience building, maintaining, and improving Data Models / Processing Pipeline / routing in life sciences environments.
- Fluency in common query languages, API development, data transformation, and integration of data streams.
- Strong experience with IQVIA data sets and other large pharma dataset platforms.
- Fluency in multiple programming languages, such as Python, Shell Scripting, SQL, Java, or similar languages and tools appropriate for large scale data processing
REQUIRED KNOWLEDGE, SKILLS AND ABILITIES: -
- Strong experience in Azure, AWS and GCP.
- Experience in big data technologies and cloud-based services for data storage and processing, data lake, data mart
- Demonstrable knowledge of SQL and relational databases, data engineering & ETL tools
Experience with acquiring data from varied sources such as: API, data queues, flat-file, remote databases.
- Understanding of traditional Data Warehouse components (e.g. ETL, Business Intelligence Tools) Creativity togo beyond current tools to deliver the best solution to the problem.
- Good Communication Skills.
PREFERRED ACADEMIC QUALIFICATION:
- Bachelor's degree in Engineering or equivalent; Master's degree added advantage.
YEARS OF EXPERIENCE - 6-8 years of experience in Data Engineering in Life Sciences with 10-12 years of cumulative experience