Data Engineer

Spanidea; Jodhpur; On-site; INR 900,000 - 1,300,000; Full time

Job summary

Spanidea in India is seeking a Data Engineer with 3–5 years of experience to design, build, and optimise scalable data pipelines using Databricks, Snowflake, and Python. The role emphasizes data ingestion, ETL/ELT development, and distributed processing.

You will work with SQL, data modeling, and integration of enterprise sources like Salesforce, ensuring data quality and performance. Collaboration with analytics teams will deliver well-structured datasets for reporting.

Qualifications

• 3-5 years of experience in data engineering or ETL development.
• Strong Python programming skills for data processing and pipeline development.
• Hands-on experience with PySpark / Spark for large-scale data processing.
• Strong SQL skills with experience writing complex queries, joins, and optimisations.
• Experience building ETL/ELT pipelines in Databricks or similar platforms.
• Experience working with Snowflake or similar cloud data warehouses.
• Understanding of data modelling and data pipeline architecture.

Responsibilities

• Design, build, and maintain ETL/ELT pipelines using Python, PySpark, and ETL tools.
• Develop data ingestion processes to integrate data from databases, APIs, and SaaS systems such as Salesforce.
• Implement data transformations and processing using Databricks (Spark/PySpark).
• Develop and optimise SQL queries and data models.
• Ensure data quality, validation, and monitoring across pipelines.
• Optimise pipelines and queries for performance and scalability.
• Collaborate with analytics teams to deliver well-structured datasets for reporting and analytics.
• Maintain documentation and version control for data workflows.

Tools

Airflow

dbt

Pentaho

Informatica

Git

Docker

Jenkins

CI/CD

AWS

Azure

GCP

Job description

Job Description: Data Engineer (Databricks / Snowflake / Python)

Experience: 3-5 Years
Location: Jodhpur, Rajasthan
Employment Type: Full-time

Role Overview:

We are looking for a Data Engineer with 3-5 years of experience to design, build, and optimise scalable data pipelines and data platforms. The role focuses on data ingestion, ETL/ELT development, and distributed data processing using Python, Databricks (Spark/PySpark), Snowflake, and modern ETL tools.

The ideal candidate should have strong experience in SQL, ETL pipeline development, and data integration, and should be comfortable working with large datasets and integrating data from enterprise systems such as Salesforce CRM.

Key Responsibilities:

• Design, build, and maintain ETL/ELT pipelines using Python, PySpark, and ETL tools.
• Develop data ingestion processes to integrate data from databases, APIs, and SaaS systems such as Salesforce.
• Implement data transformations and processing using Databricks (Spark/PySpark).
• Develop and optimise SQL queries and data models.
• Ensure data quality, validation, and monitoring across pipelines.
• Optimise pipelines and queries for performance and scalability.
• Collaborate with analytics teams to deliver well-structured datasets for reporting and analytics.
• Maintain documentation and version control for data workflows.

Required Skills:

• 3-5 years of experience in Data Engineering or ETL development.
• Strong Python programming skills for data processing and pipeline development.
• Hands-on experience with PySpark / Spark for large-scale data processing.
• Strong SQL skills with experience writing complex queries, joins, and optimisations.
• Experience building ETL/ELT pipelines in Databricks or similar platforms.
• Experience working with Snowflake or similar cloud data warehouses.
• Understanding of data modelling and data pipeline architecture.

Good to Have:

• Experience with workflow orchestration tools such as Airflow, Databricks Workflows, or Prefect.
• Experience with dbt.
• Experience with ETL tools such as Pentaho, Informatica, or similar data integration tools.
• Familiarity with Git-based version control.
• Exposure to Docker, Jenkins, and CI/CD.
• Experience working with data lake/lakehouse architectures.
• Exposure to AWS, Azure, or GCP data services.