Job description
- Total flexibility – working hours that work for YOU.
- Remote, office or hybrid – a working arrangement that works for YOU.
- Culture of learning – continuously develop YOUR knowledge and share YOUR expertise with others.
- Career Path - tailored to YOUR strengths and aspirations.
- Monthly well-being checks – to ensure you get the support you need.
- Fun, friendly working environment – experience PHASTAR’s famous social events throughout the year.
- Experience building data pipelines for various heterogenous data sources.
- Identifying, designing, and implementing scalable data delivery pipelines and automating manual processes
- Building required infrastructure for optimal data extraction, transformation and loading of data using cloud technologies like AWS, Azure etc.,
- Develop end to end processes on the enterprise level for use by the clinical data configuration specialist to prepare data extraction and transformations of raw data quickly and efficiently from various sources at the study level
- Coordinate with downstream users such as statistical programmers, SDTM programming, analytics, and clinical data programmers to ensure that outputs meet requirements of end users
- Create ELT and ETL to ingest data into data warehouse and data lakes
- Provide visualization and analysis of data stored in data lake
- Develop and maintain, tools, libraries, and reusable templates of data pipelines and standards for study level consumption by data configuration specialist
- Collaborate with various vendors and cross functional teams to build and align on data transfer specification and ensure a streamlined process of data integration
- BSc or above in life sciences, mathematics, science, engineering, or an IT related discipline.
- Experience working within life sciences/clinical trial environment (essential)
- Experience with ETL/ELT tools using SQL
- SQL, Python and Airflow experience (essential)
- Understanding of how to pull data from API
- Experience in creating end-to-end pipeline; building pipeline from scratch, creating templates; displaying data, extracting and transferring data from multiple sources to build pipelines, and creating pipelines on the cloud to allow individuals to reuse the data
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