Data Engineering Tech Lead - Pharma Commercial and Digital Marketing

Role – Data Engineering Tech Lead Mandatory

Skills:

Pharma Commercial and Digital Marketing data, PySpark, AWS Qualification & Skills Required

• 10+ years of experience in Data Engineering, Integrations, Data Modeling, Data Profiling, Data Quality, Orchestration
• Hands on expert experience in the AWS Data Engineering Tech Stack: S3, Glue, Airflow, SQL, Python, PySpark, Lambda, RDS, Redshift, EMR, and API Gateway
• Detailed experience with data modeling and data integration (ETL/ELT)
• Data Product knowledge and experience
• Pharma Commercial and Digital Marketing data experience
• Ability to lead and work through cross-functional teams
• Ability to lead and delivery project end to end working with Tech PM
• Good communication and teamwork skills

• Bachelors or Masters in Information Technology, Computer Science or relevant field.

This job operates in a professional office environment. This role routinely uses standard office equipment, including but not limited to, computers, phones, and photocopiers.

This position requires the frequent and repetitive use of a computer, keyboard, and mouse. Hand and finger dexterity is required.

Please note this job description is not designed to cover or contain a comprehensive listing of activities, duties or responsibilities that are required of the employee for this job. Duties, responsibilities, and activities may change at any time with or without notice.

Saama provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, colour, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.

This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, and training.