Data Engineer

Saur Energy International; Bengaluru; On-site; INR 600,000 - 800,000; Full time

Job summary

Saur Energy International in Bengaluru seeks a data engineer to help build and maintain data pipelines on Databricks, and to develop robust ETL/ELT processes with a focus on data quality and scalability.

You will contribute to reusable ingestion frameworks, adhere to established CI/CD practices, monitor workloads, and collaborate with Reporting, Platform, and Business teams to deliver curated datasets for downstream consumers.

Qualifications

• Bachelor's or Master's degree in Computer Science, Information Technology, or a related field.
• 1+ years of experience (including internships) in data engineering or related area.
• Hands-on knowledge of Databricks, Python (PySpark), and SQL.
• Experience building data pipelines (ETL/ELT) through projects or coursework.
• Basic understanding of CI/CD pipelines and Git-based version control.
• Familiarity with cloud platforms (AWS preferred) or willingness to learn.

Responsibilities

• Build and maintain data pipelines on Databricks under guidance.
• Develop ETL/ELT processes with emphasis on data quality and scalability.
• Contribute to reusable frameworks for data ingestion and transformation across sources.
• Follow engineering standards for pipelines, coding, and best practices.
• Assist with deploying changes through CI/CD and lifecycle validation.

Skills

Databricks

Python

SQL

ETL/ELT

CI/CD

Git

AWS

Data quality

Communication

Problem solving

Education

Bachelor's or Master's in Computer Science/IT or related field

Tools

Databricks

Git

Job description

Key Responsibilities:

Engineering & Delivery:

• Help build and maintain data pipelines on Databricks under guidance.
• Develop ETL/ELT processes with attention to data quality, consistency, and scalability.
• Contribute to reusable frameworks for ingestion, transformation, and reconciliation across source systems.
• Follow established engineering standards — coding standards, pipeline patterns, and ETL/ELT best practices.

Operations & DevOps:

• Assist with deploying changes through CI/CD and the Change Request (CR) lifecycle, including validation and ticket closure.
• Participate in problem-solving and root-cause analysis, learning to drive permanent fixes over recurring firefighting.
• Help monitor data workloads and support incident response with guidance from senior engineers.

Collaboration:

• Work with Reporting, Platform, and Business teams to help deliver curated datasets for downstream consumers.
• Communicate progress and issues clearly to engineering peers and mentors.
• Document workflows and runbooks to support reproducibility and knowledge sharing.

What Success Looks Like (First 6–12 Months):

• In your first 6–12 months, you'll build a solid understanding of the data platform, confidently deliver assigned pipeline tasks, and become comfortable with CI/CD and operational practices — with support from senior engineers.

Required Qualifications:

• Bachelor's or Master's degree in Computer Science, Information Technology, or a related field.
• 1+ years of experience (including internships) in data engineering or a related area — fresh graduates with relevant internships are encouraged to apply.
• Foundational hands-on knowledge of Databricks, Python (PySpark), and SQL for data processing.
• Exposure to building data pipelines (ETL/ELT), through projects, internships, or coursework.
• Basic understanding of CI/CD pipelines and Git-based version control.
• Familiarity with cloud platforms (AWS preferred) or willingness to learn.
• Awareness of monitoring and observability concepts.
• Good communication skills and eagerness to learn.

Preferred Qualifications:

• Exposure to orchestration frameworks or streaming technologies.
• Basic familiarity with Infrastructure-as-Code and deployment tooling.
• Awareness of observability tooling for data platforms.
• Background or interest in semiconductor manufacturing or large-scale industrial data processing.
• Any Databricks or cloud certification is a plus.

Competencies:

• Eagerness to learn and grow data engineering skills.
• Ownership mindset — takes pride in the quality of assigned work.
• Problem-solving orientation — curiosity and attention to detail.
• Collaboration — works well with peers and mentors across teams.
• Clear communication — able to explain technical details to peers.