Data Analyst

Key Responsibilities

• Analyze and process large-scale datasets from various internal systems, particularly OSS and BSS, to uncover trends and support capacity planning decisions.

• Design, develop, and maintain dashboards and data visualizations that present complex data in a clear and meaningful way for stakeholders.

• Write and optimize SQL queries to extract and manipulate structured data efficiently.

• Develop scripts (in a language of your choice) to automate data processing and transformation tasks.

• Perform data ingestion from text and flat files, ensuring accuracy, consistency, and integration into existing systems.

• Collaborate with cross-functional teams to ensure data quality and resolve data integrity issues.

• Act as a self-starter and problem-solver, capable of handling workload independently with minimal guidance from leadership.

• Apply statistical techniques such as linear and polynomial regression, forecasting analysis, and correlation analysis to develop predictive insights.

• Explore the use of AI/ML tools and libraries (e.g., PyTorch, Prophet) to enhance model accuracy and data interpretation.

• Support ad hoc reporting and data exploration requests from business stakeholders.

Required Skills & Qualifications

• Minimum 5 years of experience in a Data Analytics or Data Engineering role.

• Demonstrated experience working with large-scale datasets and managing data pipelines or ETL processes.

• Proficient in SQL, with strong experience querying complex datasets across multiple systems.

• Practical knowledge of data visualization tools and dashboarding techniques (e.g., Tableau, Power BI, Looker).

• Experience working with flat file ingestion and managing unstructured or semi-structured data.

• Ability to write scripts (language flexible – e.g., Python, Shell, etc.) to automate data processing tasks.

• Strong problem-solving capabilities and a “detective mindset” when approaching data challenges.

• Excellent communication skills, with the ability to explain technical findings to non-technical stakeholders.

• Proven ability to work independently, prioritize workload, and meet project deadlines without heavy oversight.

Preferred Skills & Experience

• Strong Python scripting skills and familiarity with advanced data analysis libraries (e.g., Pandas, NumPy, SciPy).

• Solid understanding of the data lifecycle, including data sourcing, transformation, storage, and analysis.

• Familiarity with forecasting models, t-tests, and machine learning frameworks (e.g., PyTorch, Prophet).

• Exposure to vectorization techniques, correlation modeling, and data science model validation.

• Understanding of how and why data flows through business and technical systems—both upstream and downstream.