Data Engineer

Keka Technologies Private Limited; Indore District; On-site; INR 800,000 - 1,300,000; Full time

Job summary

Keka Technologies Private Limited in India seeks a Data Engineer with 1-4 years of experience to design, build, and maintain scalable data pipelines and platforms across cloud and big data ecosystems, including Hadoop, Spark/PySpark, Snowflake, data modeling, and workflow orchestration.

Responsibilities include designing ETL/ELT pipelines, working with Hadoop/Spark, PySpark, Spark SQL, Snowflake warehouses, data modeling (Star/Snowflake), Airflow orchestration, and cloud platforms

Qualifications

• 1–4 years of relevant data engineering experience.
• Hands-on with Hadoop, Spark/PySpark, Snowflake, data modeling.
• Experience with workflow orchestration tools.

Responsibilities

• Design, develop, and maintain ETL/ELT data pipelines.
• Work with large-scale distributed systems like Hadoop and Spark.
• Develop data pipelines using PySpark and Spark SQL.
• Build and optimize Snowflake data warehouses.
• Implement workflow orchestration using Airflow.
• Collaborate with Data Analysts, Data Scientists, and Business Teams.

Skills

Hadoop

Spark/PySpark

Snowflake

Data Modeling

Airflow

Python

SQL

Tools

Spark SQL

Kafka

HDFS

Git

Job description

We are looking for a Data Engineer with 1-4 yrs years of experience to design, build, and maintain scalable data pipelines and data platforms across cloud and big data ecosystems. The candidate should have hands-on experience with Hadoop, Spark/PySpark, Snowflake, data modeling, and workflow orchestration tools.

Key Responsibilities

Design, develop, and maintain ETL/ELT data pipelines.

Work with large-scale distributed systems like Hadoop and Spark.

Develop data pipelines using PySpark and Spark SQL.

Build and optimize data warehouses in Snowflake.

Perform data modeling (Star Schema, Snowflake Schema, SCD Types).

Implement workflow orchestration using tools like Airflow.

Work with cloud platforms such as AWS / Azure / GCP.

Optimize data pipelines for performance and cost.

Ensure data quality, data validation, and monitoring.

• Work with structured and semi-structured data (JSON, Parquet, Avro, CSV).


• Collaborate with Data Analysts, Data Scientists, and Business Teams.

Required Technical Skills

Big Data & Hadoop Ecosystem

• Hadoop (HDFS, YARN, MapReduce)


• PySpark


• Hive


• HBase (optional)


• Kafka (basic knowledge)

Cloud Platforms (At least one required)

Data Warehousing

• Snowflake


• Redshift / BigQuery / Synapse (nice to have)

Data Modeling

• Snowflake Schema


• Slowly Changing Dimensions (SCD Type 1, 2, 3)

Orchestration Tools

• Azure Data Factory / AWS Step Functions / Prefect / Luigi

Programming

• Python


• PySpark


• SQL (Advanced SQL required)


• Shell Scripting (basic)

File Formats

• Parquet


• ORC


• JSON


• CSV

Version Control & CI/CD

• Git


• CI/CD pipelines (GitHub Actions / Jenkins / Azure DevOps)