Data Analyst

About the Role We are looking for a highly motivated Data Analyst / Data Engineer with 2+ years of experience in data transformation, analysis, and visualization. The ideal candidate should have a solid understanding of SQL, data modeling, ETL processes, and business intelligence tools. You will work on designing, building, and optimizing datasets and analytical solutions for e-learning and business intelligence reporting.

Key Responsibilities Design and develop data pipelines for ingestion, transformation, and reporting using SQL and Python. Create and maintain analytical tables, views, and materialized views for dashboarding and advanced analytics. Perform in-depth data analysis and validation to ensure data integrity and accuracy.

Collaborate with stakeholders to translate business requirements into technical solutions. Develop interactive dashboards and reports using tools like Power BI or Tableau. Optimize database performance (indexing, partitioning, query tuning).

Work closely with the data engineering team to improve ETL workflows and data quality monitoring. Required Skills & Qualifications 2–4 years of professional experience in data analytics or data engineering. Strong proficiency in SQL (PostgreSQL, MySQL).

Experience with ETL tools (Airflow, Dataform, or custom Python pipelines). Working knowledge of Python (Pandas, NumPy) for data transformation. Experience with data visualization tools such as Power BI Understanding of data modeling concepts (star/snowflake schema).

Familiarity with cloud databases (Azure, GCP) preferred. Strong analytical mindset and ability to work independently. Good-to-Have Experience with BigQuery, Azure Data Studio, or dbt.

Exposure to API-based data extraction and automation workflows. Knowledge of data governance and version control (Git). Interest in LMS analytics, e-learning data, or edtech domain.

What You’ll Gain Opportunity to work on end-to-end analytics lifecycle — from data ingestion to visualization. Exposure to enterprise-level data ecosystems and reporting architectures. Learn modern data engineering best practices and cloud integrations.

Work on projects impacting learning outcomes and education insights at scale.