Data Engineer (Pune/Hybrid)

Rwindia; Pune District; Hybrid; INR 1,200,000 - 1,600,000; Full time

Benefits offered by this job

Competitive compensation

Wellness benefits

Learning & development programs

Global project exposure

People‑centric, collaborative work culture

Job summary

A leading technology firm in Pune is looking for a skilled Data Engineer to design and optimize data pipelines. You will work with Python and Spark, ensuring data accuracy and deploying scalable applications with Docker. The ideal candidate has 5–7 years of experience in data engineering and a strong analytical mindset. This role offers competitive compensation, hybrid work flexibility, and opportunities for professional development in a collaborative culture.

Qualifications

• 5–7 years in a data engineering role.
• Experience working in fast‑paced, cross‑functional teams.
• Understanding of operational frameworks such as ITIL (preferred).

Responsibilities

• Design and develop scalable ETL/ELT pipelines using Spark/PySpark.
• Ensure data accuracy, validation, quality checks, and lineage across the pipeline.
• Build and deploy scalable, containerized data applications using Docker.
• Contribute to experimentation frameworks (A/B testing).

Skills

Python

Spark/PySpark

SQL

Docker

Kubernetes

Airflow

Jupyter

Azure

Education

Bachelor’s or Master’s degree in Computer Science, Statistics, Data Science or related field

Job description

Job Summary

We are hiring a skilled Data Engineer to design, build, and optimize modern data pipelines supporting analytics, experimentation, and real-time insights. This role involves working with Python, PySpark, SQL, and containerized deployments to drive high-quality data engineering outcomes. You will collaborate with cross-functional teams to ensure robust, efficient, and scalable data solutions powering key business decisions.

Key Responsibilities

1. Data Pipeline Engineering

• Design and develop scalable ETL/ELT pipelines using Spark/PySpark.
• Perform distributed data processing across large datasets.
• Write clean, optimized Python code for data extraction, transformation, and loading.
• Create reusable components and efficient data transformation logic.

2. Data Quality, Governance & Insights

• Ensure data accuracy, validation, quality checks, and lineage across the pipeline.
• Work on data profiling and visualization for insight generation.
• Support deployment of analytical and machine learning workflows.

3. Containerization & DevOps for Data

• Build and deploy scalable, containerized data applications using Docker.
• Execute and manage deployments on Kubernetes-based clusters.
• Implement CI/CD practices for data engineering workflows.

4. Experimentation & Data Products

• Contribute to experimentation frameworks (A/B testing).
• Partner with data engineering, product, and analytics teams to build data products.

Required Skills & Experience

Technical Skills (Mandatory)

• Strong experience with Python for data engineering
• Hands‑on experience with Spark / PySpark
• Strong SQL skills
• Experience with Docker and Kubernetes
• Familiarity with Airflow, Jupyter, Apache NiFi (preferred)
• Experience with Azure cloud services
• Understanding of distributed data systems, data lakes, and warehousing concepts
• Experience deploying real‑world data pipelines

Professional Experience

• 5–7 years in a data engineering role
• Experience working in fast‑paced, cross‑functional teams
• Understanding of operational frameworks such as ITIL (preferred)

Education

• Bachelor’s or Master’s degree in Computer Science, Statistics, Data Science, or related field

Personal Attributes

• Strong analytical and problem‑solving mindset
• Passion for fintech and emerging technologies
• High ethical standards and data‑driven thinking
• Effective communication and collaboration skills
• Ability to work in a dynamic, agile environment

Benefits

• Competitive compensation and wellness benefits
• Hybrid work flexibility
• Learning & development programs
• Global project exposure
• People‑centric, collaborative work culture