Data / Analytics Engineer Job Description
The Data / Analytics Engineering team empowers data-driven decision-making by developing and maintaining a robust data analytics platform to provide accurate, comprehensive, and actionable insights across the organization.
As a Data / Analytics Engineer, you will bridge the gap between data engineering and data analysis, focusing on transforming raw data into analytics-ready datasets and creating efficient, scalable data models.
Responsibilities :
This role will be responsible for :
- Partnering with Data Engineering, Data Science, and Product teams to ensure data is transformed, modeled, and ready for analysis
- Managing data transformation pipelines that provide clean, well-documented datasets for analysis
- Implementing data quality checks and monitoring to ensure the reliability of analytics outputs
- Supporting ad-hoc data requests from internal / external clients for business continuity and analytics needs
- Documenting data lineage, business logic, and data models
- Continuously learning and sharing knowledge with the team and larger Data organization
Requirements for the Role
- Bachelor's degree in Computer Science, Statistics, or a closely related field, or equivalent experience
- 2+ years of experience in data analytics, business intelligence, or related field
- Strong proficiency in SQL and experience with modern data warehouses (e.g., Snowflake, BigQuery, Redshift)
- Proficiency in Python for data analysis and manipulation
- Solid understanding of data modeling concepts (e.g., dimensional modeling, star schemas)
- Experience with version control systems (e.g., Git) and collaborative development practices
Recommended but not required abilities :
- Experience with cloud platforms (e.g., Google Cloud Platform, AWS, Azure)
- Experience with real-time analytics or streaming data processing
- Familiarity with data governance and data catalog tools
- Understanding of statistical concepts and machine learning basics
- Top-tier communication skills :
- Ability to translate business requirements into technical specifications
- Experience in communicating complex data concepts to non-technical audiences
- Demonstrated capability in extracting necessary information from meetings and written communications
- Understanding of how to navigate conflicting priorities across different teams within the company
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