Junior Data Analyst

The interview process typically starts with an online aptitude test lasting 30‑40 minutes, followed by a technical interview focusing on SQL queries, data manipulation in Python/R, and a mini case study on dashboard creation. The final HR round assesses cultural fit, communication skills, and career aspirations. Preparation should include practicing SQL joins, data cleaning scripts, and reviewing basic statistics.

The organization promotes a collaborative and inclusive work culture where learning is encouraged through regular knowledge‑sharing sessions and mentorship. Employees enjoy a good work‑life balance with flexible remote arrangements and clear growth pathways, making it an attractive place for fresh talent to thrive.

Graduates (B.Tech/B.E., B.Sc., BCA, MCA, or equivalent) from any stream with a minimum of 60% aggregate. Freshers or candidates with up to 1 year of relevant experience are welcome. No active backlogs at the time of joining. Candidates from the 2023‑2025 batch are preferred, but exceptions can be considered based on skill fit.

Round 1: Online Aptitude & Logical Reasoning Test → Round 2: Technical Interview (SQL, Python/R, BI tool case study) → Round 3: HR Interview (fitment and motivation)

The hiring organization is a fast‑growing analytics and AI solutions provider that partners with enterprises across e‑commerce, retail, and technology domains. Leveraging a blend of data engineering, machine learning, and business intelligence, the company helps clients transform raw data into actionable insights and automated decision‑making frameworks. With a culture that encourages continuous learning, collaboration, and innovation, the firm has built a reputation for delivering high‑impact projects while nurturing fresh talent. As a Junior Data Analyst, you will be the bridge between business stakeholders and the data science team. You will translate business requirements into analytical deliverables, build and maintain dashboards, and help surface insights from complex data sets. Working closely with implementation engineers, data engineers, and senior data scientists, you will gain hands‑on experience in end‑to‑end data pipeline development and machine‑learning model interpretation. Key Responsibilities: 1. Convert business requirements from BRDs into analytical outputs such as interactive dashboards and summary reports. 2. Perform data exploration, profiling, and assist data engineers in building robust data models. 3. Mentor and train junior analysts, fostering a knowledge‑sharing environment. 4. Coordinate with data engineering and data science teams to validate data against client control totals and perform analytical QA on model outputs. 5. Capture client requirements, work with product managers to refine them, and ensure deliverables meet expectations. 6. Review analytical outputs, generate actionable insights, and present findings to customers. 7. Develop and maintain BI dashboards using tools like Power BI, Tableau, or Looker. 8. Apply statistical and AI/ML concepts to interpret model results and translate them into business‑friendly narratives. 9. Communicate effectively with cross‑functional teams to ensure timely project delivery. 10. Continuously improve data processes by leveraging domain knowledge and emerging technologies. Tech Stack: SQL, Python (pandas, numpy), R, BI tools (Power BI/Tableau/Looker), Git, basic knowledge of statistical modelling and machine‑learning concepts, data warehousing fundamentals (Snowflake/Redshift), and strong communication skills. Growth Path: Starting as a Junior Analyst, you can progress to Data Analyst, Senior Analyst, and eventually to roles such as Data Engineer, Business Intelligence Lead, or Product Analytics Manager, depending on your interests and performance. Why Join: The role offers exposure to real‑world data projects, mentorship from seasoned data scientists, and the chance to work remotely while contributing to high‑impact analytics solutions. You will develop a full stack of analytical skills, from SQL querying to dashboard creation and ML insight generation, positioning you for rapid career growth in the data domain.