Data Analyst / Engineer

About Nano Interactive

Nano Interactive is a global leader in ID-Free Intent Targeting and Next-Generation Contextual Advertising. We operate at massive scale, processing over 4.9 billion live signals per day and generating 100+ GB of log data daily. The Data & Insights Team is the core engine responsible for transforming this massive, privacy-first data stream into actionable business intelligence and ensuring data quality across our proprietary LIIFT™ platform.

The Opportunity

You will join the newly formed Data & Insights Team as a crucial member, focused on the execution and maintenance of data pipelines, performing statistical analysis, and assisting in the creation of key business reports. This is an ideal role for an ambitious individual with a strong quantitative background looking to gain hands-on experience in high-volume data engineering and business intelligence within a leading Ad-Tech environment.

Key Responsibilities

• Data Pipeline Support: Assist in the development, testing, and maintenance of data transformation scripts using Python and integrating with our proprietary ETL/ELT service. Understand and manage different data types and data pipeline construction principles.
• Query Development: Write, optimize, and execute SQL queries to extract, join, and filter data from various sources (MySQL, Athena) for analysis and reporting purposes.
• Data Analysis & Reporting: Execute defined analytical tasks, perform basic statistical analysis on large datasets, and assist the team lead in creating reports and dashboards for internal teams (Client Success, Development) using our BI tools (Tableau/PowerBI).
• Problem Solving: Apply a problem-solving mentality to investigate data anomalies, interpret trends, and propose solutions for data challenges.

Required Qualifications

• Bachelor's or Master's degree in a quantitative field such as Computer Science, Data Science, Mathematics, Physics, Electrical Engineering, or Statistics.
• SQL: Solid fundamentals, including proficiency in writing efficient queries, using various joins, and filtering data.
• Python: Basic to intermediate proficiency for data manipulation and scripting (e.g., Pandas, NumPy).
• Git: Experience with basic source control (commit, push, pull, branching).
• Data Analysis: Strong understanding of how to construct and read various graphs, and the basics of how to apply statistical methods (e.g., mean, median, standard deviation, basic hypothesis testing) to extract meaningful information from raw data.
• Data Pipelines: Foundational understanding of data types, data flow, and pipeline construction principles.

Highly Valued Skills (Optional but Useful)

• Statistical Languages: Familiarity with R for statistical analysis and visualization.
• Big Data Processing: Basic exposure to or knowledge of Apache Spark.
• Cloud Basics: Familiarity with AWS cloud concepts (S3, EC2).
• Machine Learning: Basic understanding of machine learning principles.