Data Science Industrialization, Analytics Engineering Manager

Key Skills

AngularJS, Apache Airflow, AWS, Continuous Integration & Continuous Delivery - CI/CD, D3.js, Data science techniques, Data Visualization, Effective communication skills, Github, Jenkins, Python Programming, R Programming, React.js, Snowflake, SQL

Job Description

As an Analytics Engineering Manager, you will be part of the Data Science Industrialization team charged with building and maintaining industrialized analytic data and code assets that power key business applications. You will be a member of a global team developing data pipelines, optimizing feature creation, and managing and optimizing performance of data and code assets. 

ROLE RESPONSIBILITIES

• Develop, maintain, and advance at scale “industrialized” data science capabilities and analytic products
• Develop and maintain parametrized workflows, data sets, and reusable widgets in the delivery of AI/ML analytic insights products 
• Provide analytics engineering expertise to data science agile teams
• Implement CI/CD orchestration for data science pipelines
• Leverage data wrangling techniques to deliver data pipelines that Ingest and integrate data from various information sources and deliver high quality data products that drive analytics and data science applications
• Translate data needs into intro programable queries and convert Python based data wrangling code into PySpark/SQL pipelines for scalable pushdown execution
• Conduct basic data profiling and quality checks
• Fine tune performance on datasets for visualization and other applications
• Develop automated and self-monitoring data pipelines including automated QA/QC processes
• Work with stakeholders to assist with data-related technical issues and support data infrastructure needs
• Create and maintain robust technical documentation for these industrialized assets to enable knowledge retention and sharing

BASIC QUALIFICATIONS

• Bachelor’s degree in analytics engineering related area (Data Science, Computer Engineering, Computer Science, Information Systems, Engineering or a related discipline)
• 5+ years of work experience as an analyst/analytics engineer
• Strong hands-on skills in analytics engineering and data science (e.g., Python, R, SQL, industrialized ETL software)
• Experience working in a cloud based analytics ecosystem (AWS, Snowflake, etc)
• Experience working with various types of data (structured / unstructured)
• Understanding of data ingestion, data warehousing, and data model concepts
• Knowledge of relational and dimensional data structures, theories, and practices
• Highly self-motivated to deliver both independently and with strong team collaboration
• Ability to creatively take on new challenges and work outside comfort zone
• Strong English communication skills (written & verbal)

PREFERRED QUALIFICATIONS

• Advanced degree in Data Science, Computer Engineering, Computer Science, Information Systems or related discipline
• Strong hands-on skills in Javascript libraries/frameworks for visualization (D3, React, Angular)
• Experience with AI/machine learning enabling technology, such as: Data Science Studio or equivalent data science platforms, Python, R, and visualization techniques
• Hands on experience working in Agile teams, processes, and practices
• Experience in CI/CD integration (e.g. Git Hub, Git Hub Actions or Jenkins)
• Experience in software/product engineering
• Hands-on experience in containerization (e.g. AWS EKS, Kubernetes)
• Hands-on experience with data pipeline orchestration (e.g. Airflow)
• Understanding of data science development lifecycle (CRISP)
• Experience in developing and operating analytic workflows and model pipelines that are parametrized, automated and reusable
• Experience developing and deploying data and analytic products for use by technical and non-technical audiences
• Pharma & Life Science commercial functional knowledge
• Pharma & Life Science commercial data literacy
• Experience with Dataiku Data Science Studio