ABOUT THE ROLE
Role Description:
The role is responsible for designing, building, maintaining, analyzing, and interpreting data to provide actionable insights that drive business decisions. This role involves working with large datasets, developing reports, supporting and executing data governance initiatives and, visualizing data to ensure data is accessible, reliable, and efficiently managed. The ideal candidate has strong technical skills, experience with big data technologies, and a deep understanding of data architecture and ETL processes
Roles & Responsibilities:
- Design, develop, and maintain data solutions for data generation, collection, and processing
- Be a key team member that assists in design and development of the data pipeline
- Create data pipelines and ensure data quality by implementing ETL processes to migrate and deploy data across systems
- Contribute to the design, development, and implementation of data pipelines, ETL/ELT processes, and data integration solutions
- Take ownership of data pipeline projects from inception to deployment, manage scope, timelines, and risks
- Collaborate with cross-functional teams to understand data requirements and design solutions that meet business needs
- Develop and maintain data models, data dictionaries, and other documentation to ensure data accuracy and consistency
- Implement data security and privacy measures to protect sensitive data
- Leverage cloud platforms (AWS preferred) to build scalable and efficient data solutions
- Collaborate and communicate effectively with product teams
- Collaborate with Data Architects, Business SMEs, and Data Scientists to design and develop end-to-end data pipelines to meet fast paced business needs across geographic regions
- Adhere to best practices for coding, testing, and designing reusable code/component
- Explore new tools and technologies that will help to improve ETL platform performance
- Participate in sprint planning meetings and provide estimations on technical implementation
Basic Qualifications and Experience:
- Master’s degree and 1 to 3 years of Computer Science, IT or related field experience OR
- Bachelor’s degree and 3 to 5 years of Computer Science, IT or related field experience OR
- Diploma and 7 to 9 years of Computer Science, IT or related field experience
Functional Skills:
Must-Have Skills
- Proficiency in Python, PySpark, and Scala for data processing and ETL (Extract, Transform, Load) workflows, with hands-on experience in using Databricks for building ETL pipelines and handling big data processing
- Experience with data warehousing platforms such as Amazon Redshift, or Snowflake.
- Strong knowledge of SQL and experience with relational (e.g., PostgreSQL, MySQL) databases.
- Familiarity with big data frameworks like Apache Hadoop, Spark, and Kafka for handling large datasets.
- Experienced with software engineering best-practices, including but not limited to version control (GitLab, Subversion, etc.), CI/CD (Jenkins, GITLab etc.), automated unit testing, and Dev Ops
Good-to-Have Skills:
- Experience with cloud platforms such as AWS particularly in data services (e.g., EKS, EC2, S3, EMR, RDS, Redshift/Spectrum, Lambda, Glue, Athena)
- Experience with Anaplan platform, including building, managing, and optimizing models and workflows including scalable data integrations
- Understanding of machine learning pipelines and frameworks for ML/AI models
Professional Certifications:
- AWS Certified Data Engineer (preferred)
- Databricks Certified (preferred)
Soft Skills:
- Excellent critical-thinking and problem-solving skills
- Strong communication and collaboration skills
- Demonstrated awareness of how to function in a team setting
- Demonstrated presentation skills