ETL Specialist (Workflow and Automation)

About this role:

As a Specialist in Data Science team, you will be responsible for designing, developing, and maintaining the infrastructure and systems required for data storage, processing, and analysis. This is a crucial role in building and managing the data pipelines that enable efficient and reliable data integration, transformation, and delivery for data users across the team.

What you will do:

• Designs and develops data pipelines that extract data from various sources, transform it into the desired format, and load it into the appropriate data storage systems
• Collaborates with data scientists and analysts to optimize models and algorithms for data quality, security, and governance
• Integrates data from different sources, including databases, data warehouses, APIs, and external systems
• Ensures data consistency and integrity during the integration process, performing data validation and cleaning as needed
• Transforms raw data into a usable format by applying data cleansing, aggregation, filtering, and enrichment techniques
• Optimizes data pipelines and data processing workflows for performance, scalability, and efficiency
• Monitors and tunes data systems, identifies and resolves performance bottlenecks, and implements caching and indexing strategies to enhance query performance
• Implements data quality checks and validations within data pipelines to ensure the accuracy, consistency, and completeness of data
• Establishes and enforces data standards, naming conventions, and data classification frameworks
• Establishes the governance of data and algorithms used for analysis, analytical applications, and automated decision making

What you will need:

• Masters degree in computer science, data science, software engineering, information systems, or related quantitative field
• 0-2 years of professional experience in data management disciplines, including data integration, optimization and data quality, or other areas directly relevant to data responsibilities and tasks
• Ability to collaborate within and across teams of different technical knowledge and work across business lines to describe business use cases/outcomes, data sources and management concepts, and analytical approaches/options
• Experience in building and maintaining data systems/databases from multiple sources
• Ability using one or more:
• ETL tools for data integration and workflow automation
• Python, R and relational DB (SQL, NoSQL, etc.), SPSS
• APIs and web scraping tools to gather data from various sources