Senior Data Engineer

Iris Software Inc.; Dadri; On-site; INR 1,500,000 - 2,500,000; Full time

Job summary

Iris Software Inc. is seeking a senior data engineer in Noida to design and implement scalable data pipelines using PySpark, Databricks, and Kafka. You will shape ingestion, transformation, and orchestration architectures, ensuring data quality and governance across enterprise platforms.

The role emphasizes hands-on development with Snowflake or Delta Lake on Databricks, and collaboration with cross-functional teams to deliver high-value data products while leveraging modern Lakehouse principles.

Qualifications

• Experience: 5-8 years in data engineering.
• Location: Noida, India.
• Mandatory skills: Python, PySpark, Databricks, Kafka.

Responsibilities

• Design scalable data engineering solutions with PySpark and distributed processing.
• Define ingestion, transformation, and processing architectures aligned to analytics.
• Design and optimize Snowflake or Delta Lake on Databricks for enterprise-scale platforms.
• Lead high-performance batch and streaming data pipelines.
• Architect event-driven architectures using Kafka or Kinesis.
• Define streaming standards and scalable processing patterns.
• Use Airflow or Databricks Workflows for orchestration.
• Establish monitoring and governance for reliable pipelines.
• Drive data quality, documentation, and trusted data consumption.

Skills

Python

PySpark

Databricks Workflows

Delta Lake on Databricks

Apache Kafka

AWS CloudShell

Azure Databricks

Tools

Airflow

Databricks

Snowflake

Kinesis

Kafka

Job description

Job Description:

Why Join Iris?
Are you ready to do the best work of your career at one of India’s Top 25 Best Workplaces in IT industry? Do you want to grow in an award-winning culture that truly values your talent and ambitions?
Join Iris Software — one of the fastest-growing IT services companies— where you own and shape your success story.

About Us

At Iris Software, our vision is to be our client’s most trusted technology partner, and the first choice for the industry’s top professionals to realize their full potential.

With over 4,300 associates across India, U.S.A, and Canada, we help our enterprise clients thrive with technology-enabled transformation across financial services, healthcare, transportation & logistics, and professional services.

Our work covers complex, mission-critical applications with the latest technologies, such as high-value complex Application & Product Engineering, Data & Analytics, Cloud, DevOps, Data & MLOps, Quality Engineering, and Business Automation.

Working with Us

At Iris, every role is more than a job — it’s a launchpad for growth.

Our Employee Value Proposition, “Build Your Future. Own Your Journey.”reflects our belief that people thrive when they have ownership of their career and the right opportunities to shape it.

We foster a culture where your potential is valued, your voice matters, and your work creates real impact. With cutting-edge projects, personalized career development, continuous learning and mentorship, we support you to grow and become your best — both personally and professionally.

Curious what it’s like to work at Iris? Head to this video for an inside look at the people, the passion, and the possibilities. Watch it here.

Job Description

Experience: 5-8 Years

Location: Noida

Mandatory Skills:

Python, PySpark, Databricks Workflows, Delta Lake on Databricks, Apache Kafka

Additional Skills:

AWS CloudShell, Azure Databricks

Key Responsibilities

• Design scalable data engineering solutions using PySpark and modern distributed data processing frameworks.
• Define data ingestion, transformation, and processing architectures aligned with business and analytical objectives.
• Design and optimize Snowflake or Delta Lake on Databricks solutions to support enterprise-scale data platforms.
• Lead implementation of high-performance batch and streaming data pipelines.
• Design and optimize event-driven data architectures using Apache Kafka or Amazon Kinesis.
• Define data streaming standards, integration frameworks, and scalable processing patterns.
• Architect workflow orchestration solutions using Apache Airflow or Databricks Workflows.
• Establish monitoring, scheduling, and operational controls for reliable pipeline execution.
• Drive data quality, validation, reconciliation, and governance practices across data engineering solutions.
• Design data engineering solutions following modern Lakehouse architecture principles, data observability practices, and platform engineering standards to improve scalability, reliability, and operational visibility.
• Drive development of business-focused data products by improving data quality, discoverability, usability, documentation, and trusted data consumption across analytical platforms.
• Promote responsible use of AI-assisted engineering capabilities to improve development productivity, testing, documentation, and engineering quality.
• Review data pipeline designs and implementations to ensure adherence to engineering, scalability, and performance standards.
• Troubleshoot complex data processing, workflow, and streaming platform issues through detailed root cause analysis.
• Mentor team members on PySpark, Snowflake, Delta Lake, Kafka, Kinesis, Airflow, and data engineering best practices.
• Collaborate with various teams and stakeholders to support end-to-end data platform delivery.

Behavioral Competencies

• Demonstrates strong ownership while driving data engineering excellence.
• Collaborate effectively with various teams and business stakeholders to ensure smooth delivery.
• Promotes quality-focused engineering through proactive validation, optimization, and continuous improvement.
• Apply strong analytical thinking to evaluate complex data engineering and platform challenges.
• Demonstrate adaptability while managing evolving technologies, data ecosystems, and business requirements.
• Communicates effectively regarding delivery status, risks, dependencies, and improvement opportunities.
• Maintains high attention to detail across data architecture, pipeline design, testing, and implementation activities.
• Encourages continuous improvement in data engineering practices and platform operations.
• Supports knowledge sharing and mentoring to strengthen team capabilities.
• Balances scalability, performance, reliability, and business priorities while driving delivery excellence.
• Promotes innovation by adopting modern data engineering practices, platform engineering principles, and AI-assisted development approaches to improve engineering productivity and solution quality.

Perks and Benefits for Irisians

Iris provides world-class benefits for a personalized employee experience. These benefits are designed to support financial, health and well-being needs of Irisians for a holistic professional and personal growth. Click here to view the benefits.