Iris Software Inc.
Website:
irissoftware.com
Company:
https://www.linkedin.com/company/iris-software-inc.
Seniority: Not Applicable
Industries: IT Services and IT Consulting
Job details:
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
Mandatory Skills:
Databricks Workflows, PySpark, Delta Lake on Databricks, Apache Kafka
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.
Mandatory Competencies
Data Science and Machine Learning - Data Science and Machine Learning - Python
Data Science and Machine Learning - Data Science and Machine Learning - Apache Spark
Data & AI - Data Engineering - Data Quality & Validation
Big Data - Big Data - Pyspark
Database - Database Programming - SQL
Data & AI - Data Engineering - Apache Kafka
Data Science and Machine Learning - Data Science and Machine Learning - Databricks
Cloud - AWS - AWS S3, S3 glacier, AWS EBS
Cloud - AWS - Dynamo DB, Amazon Aurora, Amazon RDS
Beh - Communication and collaboration
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.
Click on Apply to know more.