Senior Engineer, Data Engineering

2 days ago

Pune, Maharashtra, India InfoBeans Full time
Greetings from Infobeans (

Note: We are hiring immediate/ early joiner with relevant experience in Python.

About the Role

We are seeking a Senior Data Engineer with a passion for building scalable data infrastructure, fluent Python skills, and a sharp analytical mindset. You will design, build, and optimize our data pipelines and architectures to support critical data-driven initiatives across the business. Your work will directly impact the quality and accessibility of our analytics, data science, and reporting systems.

Key Responsibilities

Architect and maintain scalable, reliable data pipelines for batch and real-time processing.

Write high-quality Python code to manipulate and transform large datasets.

Implement data modeling, ETL processes, and orchestration frameworks (e.g., Airflow, Prefect).

Ensure data quality, consistency, and documentation across systems and stakeholders.

Collaborate closely with data analysts, data scientists, and engineering teams to support business needs.

Optimize performance of databases and data warehouses (MS SQL Server).

Monitor, troubleshoot, and resolve issues in production pipelines.

Required Qualifications

~ Bachelors and/or masters degree in Computer Science

~5+ years of experience in data engineering or backend development with data-intensive systems.

~ Fluent in Python — with strong proficiency in pandas, API integration.

~ Deep understanding of data structures, algorithms, and best practices in clean code and testing.

~ Proven experience with modern data stacks (e.g., Airflow, Fivetran, Stitch).

~ Strong SQL skills and experience with data warehousing concepts.

~ Experience working with AWS cloud platform.

~ Detail-oriented mindset — able to maintain rigor in data quality and documentation.

~ Strong analytical and problem-solving skills — able to break down complex problems into data solutions.

Nice-to-Have

~ Experience with ML model pipelines or supporting data science workflows.

Exposure to CI/CD for data pipelines.

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