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.