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.