Data and Business Intelligence Analyst

Primary Job Responsibilities

1. Data Engineering & Pipeline Development: Design, build, and maintain scalable and reliable data pipelines using Python and related technologies to extract, transform, and load (ETL) data from various sources.
2. Data Modeling & Warehousing: Participate in the design and implementation of data models and data warehousing solutions to ensure efficient data storage and retrieval for analytical purposes.
3. Business Intelligence Development: Develop and maintain interactive dashboards, reports, and data visualizations using BI tools (e.g., Tableau, Power BI, Looker) to monitor key business metrics and provide actionable insights.
4. Python for Data Analysis & Automation: Utilize Python and relevant libraries (e.g., Pandas, NumPy, SciPy) to perform in-depth data analysis, statistical modeling, and automate reporting and data processing tasks.
5. Requirements Gathering & Stakeholder Collaboration: Work closely with business stakeholders to understand their data and reporting requirements and translate them into technical specifications.
6. Data Quality & Governance: Implement and monitor data quality processes to ensure accuracy, consistency, and integrity of data used for analysis and reporting. Adhere to data governance policies.
7. Performance Optimization: Identify and implement optimizations to data pipelines and BI solutions to improve performance and efficiency.
8. Documentation & Knowledge Sharing: Create and maintain comprehensive documentation for data pipelines, BI solutions, and analytical processes. Share knowledge and best practices with the team.
9. Ad-hoc Analysis & Problem Solving: Conduct ad-hoc data analysis to answer specific business questions and troubleshoot data-related issues.

Qualifications:

1. Bachelor's degree in Computer Science, Engineering, Statistics, Mathematics, or a related quantitative field.
2. 4-7 years of hands-on experience in a data-focused role with significant responsibilities in data engineering, business intelligence, and utilizing Python for data tasks.
3. Product development exposure: Experience working on analytics or data products throughout the product lifecycle — from requirements to delivery.
4. Knowledge of Python and relevant data manipulation and analysis libraries (e.g., Pandas, NumPy). Experience with libraries for data visualization (e.g., Matplotlib, Seaborn) is a plus.
5. Solid understanding of relational databases (e.g., SQL Server, PostgreSQL, MySQL) and excellent SQL skills for data querying and manipulation.
6. Experience designing, building, and maintaining ETL pipelines.
7. Proven ability to develop compelling and insightful data visualizations using BI tools such as Tableau, Power BI, or Looker (specify preferred tool if applicable).
8. Proven ability to support and improve analytical products that drive business decisions
9. Familiarity with data warehousing concepts and different data modeling techniques.
10. Strong analytical and problem-solving skills with the ability to work with complex datasets.
11. Good communication and collaboration skills.
12. Experience with version control systems (e.g., Git) is a plus.