Associate Data Engineer

Overview

We are looking for a motivated and detail-oriented Associate Data Engineer to join our data team. This role is ideal for recent graduates or early-career professionals (up to 1 year of experience) with strong Python and SQL fundamentals and a genuine interest in data engineering, cloud technologies, and the modern data stack.

You will work alongside experienced data engineers on real-world projects, gaining hands-on experience in building data pipelines, processing data at scale, and working with cloud-based data platforms. This role offers structured mentorship and a clear growth path toward becoming a Data Engineer.

Key Responsibilities

Data Processing and Pipelines

Write clean, well-structured Python scripts for data processing, transformation, and automation.

Assist in building and maintaining ETL/ELT pipelines and data workflows.

Work with structured, semi-structured (JSON, CSV, Parquet), and unstructured data from multiple sources such as APIs, databases, and files.

Perform data cleaning, transformation, and validation using libraries such as Pandas or Polars.

Databases and SQL

Write and optimize SQL queries for data extraction, analysis, and transformation.

Work with relational databases such as PostgreSQL, MySQL, or SQL Server.

Assist in designing simple data models and database tables.

Data Quality and Support

Help implement data quality checks to ensure accuracy and consistency.

Monitor scheduled jobs and help troubleshoot pipeline failures and data issues.

Support data integration activities across systems and teams.

Cloud and Tools Exposure

Gain hands-on exposure to cloud data services on AWS, Azure, or GCP (such as S3, Azure Data Factory, or BigQuery).

Learn and work with orchestration tools such as Apache Airflow and transformation tools such as dbt.

Get introduced to modern data platforms such as Databricks, Snowflake, or Microsoft Fabric.

Collaboration and Learning

Work closely with senior team members to understand requirements and deliver assigned tasks.

Participate in code reviews, team discussions, and knowledge-sharing sessions.

Maintain clear documentation of data processes, pipelines, and technical work.

Continuously learn new tools, frameworks, and best practices in data engineering.

Required Skills

Strong understanding of Python fundamentals, including data structures, functions, file handling, and error handling.

Good working knowledge of SQL, including joins, aggregations, GROUP BY, and subqueries.

Basic understanding of relational databases and data modeling concepts (tables, keys, normalization).

Basic understanding of data processing concepts and ETL/ELT workflows.

Familiarity with working with data formats such as CSV, JSON, and Excel.

Familiarity with Git and GitHub.

Good analytical skills and attention to detail.

Clear written and verbal communication.

A strong willingness to learn and adapt quickly.

Good to Have

Hands-on experience with Pandas, NumPy, or Polars.

Knowledge of advanced SQL concepts such as window functions and CTEs.

Basic exposure to Apache Spark / PySpark.

Familiarity with Airflow, dbt, or similar data tools.

Basic exposure to a cloud platform (AWS, Azure, or GCP).

Basic understanding of data warehousing concepts (fact and dimension tables, star schema).

Familiarity with NoSQL databases such as MongoDB.

Basic knowledge of Docker or Linux commands.

Exposure to BI tools such as Power BI or Tableau.

Relevant certifications (such as AWS Cloud Practitioner, Azure Data Fundamentals DP-900, or Databricks Fundamentals), Kaggle work, or hackathon participation.

A GitHub profile or portfolio showcasing data or Python projects.

Educational Qualifications

Bachelor’s degree in Computer Science, Information Technology, Data Science, Engineering, or a related technical field.

Academic, internship, or personal projects involving Python, SQL, databases, data engineering, or cloud technologies will be strongly considered.

What We’re Looking For

Strong fundamentals, a solid educational background, and a logical, problem-solving mindset.

Candidates who can clearly explain their academic or personal projects, including the data they worked with, the tools they used, and what they learned.

Curiosity, attention to detail, and the ability to learn quickly in a fast-evolving field.

Extensive professional experience is not required; we value potential, attitude, and a passion for working with data.

Pay

₹300,000.00 - ₹450,000.00 per year

Benefits

Health insurance

Leave encashment

Provident Fund

Work Location

In person