Senior Data Engineer

Lever, Inc.; India; Remote; INR 2,000,000 - 4,000,000; Full time

Job summary

Lever, Inc. is looking for a data engineer to design and implement scalable data pipelines in a remote role located in India.

The candidate will leverage Microsoft Azure Fabric and Databricks to deliver enterprise-grade data solutions, collaborating with business and technical stakeholders. Ideal for professionals with 5+ years of hands-on experience in data engineering, SQL, data modeling, and Python/Spark skills, who can manage cloud-based data platforms in production environments and own

Qualifications

• 5+ years hands-on experience as a Data Engineer.
• Strong experience with Microsoft Azure Fabric, Databricks, and Azure data services.
• Solid knowledge of SQL and data modeling concepts.
• Experience building enterprise-grade data pipelines and data integration solutions.
• Proficiency in Python, Spark, or similar technologies.
• Experience with cloud-based data platforms in production environments.

Responsibilities

• Design, develop, and maintain scalable data pipelines on Azure Fabric.
• Build reliable ETL/ELT processes for structured and unstructured data.
• Collaborate with business teams to translate data requirements into solutions.
• Optimize data processing for performance, scalability and cost.
• Troubleshoot production issues and identify improvements.
• Work with architects, analysts, and developers throughout the project lifecycle.
• Owns their work and delivers on time with quality.

Skills

Data Engineering

Databricks

Azure Fabric

SQL

Data Modeling

Python

Spark

Cloud Data Platforms

Tools

Azure Data Factory

Azure Data Services

Job description

This role is for one of our clients

Industry: Software Development

Seniority level: Mid-senior level

Min Experience: 5+ years
Location: India
JobType: full-time

₹20,00,000 - ₹40,00,000 a year

This is a full-time Remote role for an experienced Data Engineer who enjoys solving complex data problems and takes ownership from design through delivery. You can work from home (anywhere in INDIA, job location is in India).

This role is ideal for someone who can build scalable data pipelines on Microsoft Azure Fabric, leverage AI to improve engineering productivity, and work closely with business and technical stakeholders.

Requirements

Qualifications:

• 5+ years of hands-on experience as a Data Engineer.
• Strong experience with Microsoft Azure Fabric, Databricks and Azure data services.
• Solid knowledge of SQL and data modeling concepts.
• Experience building enterprise-grade data pipelines and data integration solutions.
• Proficiency in Python, Spark, or similar technologies is preferred.
• Experience working with cloud-based data platforms in production environments.

Key Skills:

• Design, develop, and maintain scalable data pipelines using Microsoft Azure Fabric.
• Build reliable ETL/ELT processes for structured and unstructured data.
• Work with business teams to understand data requirements and translate them into technical solutions.
• Optimize data processing workflows for performance, scalability, and cost.
• Apply AI-assisted development tools and modern engineering practices to improve delivery speed and solution quality.
• Troubleshoot production issues and proactively identify improvements.
• Collaborate with architects, analysts, and developers throughout the project lifecycle.
• Owns their work. Take responsibility for quality, timelines, and outcomes without constant follow-up.
• Thinks beyond the obvious. Actively use AI and automation to solve problems, improve engineering productivity, and deliver better solutions.
• Communicates proactively. Raise risks early, ask questions when something isn't clear, and keep stakeholders informed.
• Adapts quickly. Requirements evolve, and you're comfortable adjusting priorities while maintaining delivery commitments.
• Works collaboratively. Available to engage with global teams and can overlap with the U.S. East Coast during critical discussions (up to 1:00 PM ET, as needed).

Must-have skills

Databricks, Fabric, Data Engineering