KASHII UPDATEZ Everyday Student Requirements & Python Coding Tutorials by Python Kashi
JD #2383
Applications open Full-Time Job

Databricks – Engineer

IRIS Software

  • Noida, Uttar Pradesh, India
  • 66 - 69 LPA
  • Fresher (0-1 Yrs)
  • Posted Today
HR
Recruiter contact
Recruiter / hiring contact
No public email in this post. Use a source-linked contact instead.

About the role

IRIS Software is a prominent Indian IT services firm that delivers end‑to‑end technology solutions across sectors such as financial services, healthcare, logistics and professional services. With a strong focus on application and product engineering, data & analytics, cloud, DevOps and AI‑driven operations, the company has built a reputation for handling complex enterprise challenges at scale. Its culture encourages continuous learning, collaborative problem‑solving and a quality‑first mindset, making it an attractive destination for fresh engineering talent. The Databricks Engineer role is positioned within IRIS Software’s Data Engineering practice. The position is aimed at fresh graduates who are eager to design, develop and maintain robust data pipelines on modern lakehouse platforms. Candidates will work on both batch and near‑real‑time data processing, leveraging PySpark, Apache Kafka, Databricks Workflows, Delta Lake and Snowflake to deliver reliable, business‑ready data products. The role also expects engineers to adopt AI‑assisted coding tools, ensure thorough testing, and provide production support for critical data workflows. Key Responsibilities: 1. Design, develop and maintain scalable PySpark pipelines for batch and streaming workloads. 2. Build ingestion and transformation workflows using Apache Kafka or Amazon Kinesis for event‑driven data movement. 3. Configure and manage Databricks Workflows and/or Apache Airflow for job orchestration and scheduling. 4. Implement data storage solutions on Delta Lake (Databricks) and Snowflake, ensuring data quality and consistency. 5. Perform data validation, reconciliation and quality checks to guarantee reliable outputs. 6. Troubleshoot pipeline failures, conduct root‑cause analysis and optimise performance bottlenecks. 7. Write unit and integration tests, support CI/CD deployments and provide production support. 8. Document technical designs, workflow definitions and operational procedures. 9. Collaborate with cross‑functional teams and business stakeholders to translate requirements into data solutions. 10. Leverage approved AI‑assisted engineering tools to improve productivity while adhering to coding standards. Tech Stack: PySpark, Apache Kafka, Databricks Workflows, Delta Lake, Snowflake, Apache Airflow, Amazon Kinesis, Python, SQL, Git. Growth Path: Freshers start as Data Engineers, progress to Senior Data Engineer, then to Data Platform Lead or Solution Architect, with opportunities to specialise in lakehouse architectures, real‑time analytics or cloud data platforms. Why Join IRIS Software? The company offers exposure to cutting‑edge data technologies, mentorship from seasoned engineers, and a clear career ladder. Employees benefit from a supportive work culture that balances challenging projects with work‑life harmony, competitive compensation and a suite of health and wellness benefits. Joining IRIS Software provides a solid foundation for a long‑term career in data engineering and analytics.

Eligibility & process

Who can apply

Degree: B.E. / B.Tech in Computer Science, Information Technology, Electronics, Telecommunication or any related engineering branch. Minimum aggregate: 60% (or equivalent CGPA). Batch: 2026 (or graduating in 2026). No active backlogs at the time of application. Freshers eligible as per recruitment summary; candidates should verify any additional criteria on the official portal.

Skills & stack

Pyspark Apache Kafka Databricks Workflows Delta Lake Snowflake Apache Airflow Amazon Kinesis Data Validation

Selection process

  1. Round 1: Online Aptitude Test
  2. Round 2: Technical Interview (Data Engineering concepts, coding in PySpark/Python)
  3. Round 3: HR Interview

Prep kit

19 resources
Study material 4
Company-specific guides 4
Likely coding questions 4
Round-wise strategy 4
Round 1: Online Assessment (OA) Aptitude, Quantitative Logic & 2 Coding Problems Focus on accuracy and speed. Practice arrays, strings, and standard arithmetic puzzles.
Round 2: Technical Interview 1 Core Tech Stack (Pyspark, Apache Kafka, Databricks Workflows, Delta Lake, Snowflake, Apache Airflow, Amazon Kinesis, Data Validation) & Live Code Tracing Explain your thought process aloud. Analyze time & space complexities before coding.
Round 3: System Design & Problem Solving Database Schemas, APIs & Architecture Basics Clarify edge cases, diagram schemas cleanly, and discuss scalability trade-offs.
Round 4: HR & Cultural Fit Discussion IRIS Software Core Values, Learning Agility & Offer Terms Demonstrate passion, strong communication, and readiness for full-time collaboration.
Technical questions 4
What is the bias-variance tradeoff and how do you prevent overfitting? Answer
Model answer: High bias leads to underfitting (oversimplified model), high variance leads to overfitting (captures noise). Mitigate using L1/L2 Regularization, Dropout, Cross-Validation, and data augmentation.
Explain the difference between Precision, Recall, and F1-Score. Answer
Model answer: Precision = TP / (TP + FP) (correctness of positive predictions). Recall = TP / (TP + FN) (coverage of actual positives). F1-Score is the harmonic mean of Precision and Recall.
How does Gradient Descent work and what is the role of Learning Rate? Answer
Model answer: It optimizes loss functions by iteratively moving weights in the direction of negative gradient. A large learning rate may overshoot the minimum; a small rate causes slow convergence.
What is the difference between Supervised, Unsupervised, and Self-Supervised learning? Answer
Model answer: Supervised uses labeled data (X -> y). Unsupervised finds hidden patterns in unlabeled data (clustering/PCA). Self-supervised generates labels from input data (e.g. masked language modeling in BERT/Transformers).
Behavioural & HR questions 3
Why do you want to join IRIS Software as a Databricks – Engineer?
Highlight IRIS Software's market reputation, recent tech innovations, and how your skills in Pyspark directly solve their team's objectives.
Describe a challenging bug or academic project roadblock and how you resolved it.
Use the STAR method: Situation (project context), Task (what needed solving), Action (specific tools/logic applied), Result (quantifiable positive outcome).
How do you handle strict deadlines or sudden scope changes?
Explain your prioritization strategy, proactive communication with mentors/peers, and agile mindset.
IRIS Software 66 - 69 LPA
Recruiter Resume Apply
Chat Chat with Kashii