Senior Data Analyst

Position Summary

The Data Analyst plays a critical role in transforming raw data into meaningful insights

that drive strategic decision-making and operational efficiency. This role involves

advanced data analysis, statistical modeling, and the application of machine learning

techniques to solve complex business problems. The ideal candidate will be highly

analytical, technically proficient, and capable of leveraging data to create a strategic

advantage.

Key Duties and Responsibilities

Data Acquisition & Preparation

 Source, clean, and integrate data from diverse internal and external sources,

ensuring accuracy, consistency, and completeness.

 Develop ETL pipelines to automate data preparation and improve data readiness for

analytics.

Advanced Data Analysis & Modeling

 Apply statistical methods, predictive modeling, and machine learning algorithms to

uncover patterns and forecast trends.

 Design and implement A/B testing and experimental frameworks to evaluate

business hypotheses.

Data Visualization & Storytelling

 Build interactive dashboards and visualizations using tools such as Tableau, Power

BI, or Looker to communicate findings effectively to stakeholders.

 Present analytical insights in a clear, compelling manner to both technical and non-

technical audiences.

Business Intelligence & Reporting

 Develop and deliver scheduled and ad-hoc reports that translate complex data into

key business insights.

 Collaborate with stakeholders to define KPIs and data-driven metrics that align with

business objectives.

Data Quality & Governance

 Establish and uphold data quality standards; proactively identify and resolve data

inconsistencies or anomalies.

 Contribute to the development of data governance frameworks and best practices.

Cross-Functional Collaboration

 Work closely with cross-departmental teams—including product, marketing,

operations, and IT—to understand business needs and deliver actionable insights.

 Guide data-informed decision-making and foster a data-driven culture.

Continuous Learning & Innovation

 Stay current with emerging tools, technologies, and best practices in the fields of

data science, analytics, and AI.

 Explore new techniques in deep learning, NLP, and real-time analytics to enhance

the organization's data capabilities.

Required Qualifications

 Bachelor's or Master's degree in Data Science, Computer Science, Statistics,

Mathematics, or a related field.

 Minimum of 3 years of experience in business intelligence

 Practical ability to visualize data, communicate the data, and utilize it effectively

 3 years of experience in database building or other programming experience

 Strong background in statistical analysis, machine learning, and data mining

techniques.

 Proficiency in SQL, Python, and statistical packages such as R, SPSS, or SAS.

 Solid understanding of databases and experience working with large, complex

datasets.

 Familiarity with data visualization platforms (e.g., Power BI, Tableau, Looker).

 Experience with web scraping and working with e-commerce platforms such as

Shopify or Magento is a plus.

 Strong problem-solving skills with the ability to work on multiple projects

simultaneously.

 Excellent verbal and written communication skills for presenting technical concepts

clearly and persuasively.

Preferred Skills & Competencies

 Hands-on experience with data engineering tools.

 Exposure to MLOps, version control (e.g., Git), and CI/CD pipelines for model

deployment.

 Strong attention to detail and a commitment to delivering high-quality results.

 Ability to work effectively under pressure and meet tight deadlines.

 A collaborative mindset with a strong desire to learn and share knowledge.

Benefits

 Annual Performance Bonuses

 Competitive Compensation Package

 9 Working Hours per Day

 2 Days Off Weekly

 Professional Development Opportunities

 Dynamic and Supportive Work Environment