Data Analyst job at ShiftCode Analytics Chicago, IL, US

Job Description

Interview : Video Visa : USC, GC, GC EAD, H4, L2 This is hybrid from day-1 Description : We are seeking a highly motivated and skilled Data Analyst to join our dynamic team. If you are passionate about data, proficient in Python and SQL, and have experience working with cloud platforms such as Azure, AWS, or GCP. Responsibilities:

Data Analysis and Interpretation: Collect, analyze, and interpret data to identify trends and patterns affecting our Ads Taxonomy table. Translate data findings into actionable insights and recommendations for the core team.
Data Collection and Strategy: Develop and implement effective data collection systems and strategies to support automation and integration projects. Ensure data accuracy, availability, integrity and reliability for various initiatives.
SQL Expertise: Utilize efficient SQL writing and querying techniques to troubleshoot issues directly within the Snowflake data warehouse. Optimize queries for performance and maintainability.
Scripting and Automation: Execute Shell/Python scripts using command line tools to fulfill data delivery requirements, reruns, and restatements. Leverage Python scripting to automate tasks and processes.
Data Workflows and Insights: Work with data tools including SQL, Tableau, and Python to automate data workflows. Build meaningful insights through data visualizations, standardized reports, and dashboards for ad-hoc analyses.

Requirements:

Bachelor's degree in a relevant field (e.g., Computer Science, Data Science, Statistics).
Proven experience as a Data Analyst or similar role.
Proficiency in Python and SQL for data analysis and manipulation.
Familiarity with data visualization libraries (e.g., Matplotlib, Seaborn).
Experience with cloud platforms, such as Azure, AWS, or GCP.
Strong problem-solving skills and attention to detail.
Excellent communication and presentation skills.
Ability to work independently and collaboratively in a team environment.
Knowledge of machine learning techniques (preferred but not required).

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