Junior Data Engineer

Job Description: Data Analyst/Data Engineer (2+ Years Experience)
Type: Full-time
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.