Director - Data Engineering - Pharma

YEARS OF EXPERIENCE:

- 6-8 years of experience in Data Engineering in Life Sciences with 10-12 years of cumulative experience

SPECIFIC RESPONSIBILITIES:-

- 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.

REQUIRED KNOWLEDGE, SKILLS AND ABILITIES: -

- 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.

- 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

- Fluency in multiple programming languages, such as Python, Shell Scripting, SQL, Java, or similar languagesand tools appropriate for large scale data processing.

- 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 to go 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.