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Data Integration Engineer at HEXAWARE
, , India -
Full Time

Start Date

Immediate

Expiry Date

17 Sep, 26

Salary

0.0

Posted On

19 Jun, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

Python, SQL, Pandas, Agentic Workflows, Polars, Window Functions, ETL/ELT, Data Warehousing, PLSQL, Parallel Processing, Data Validation, Pipeline Optimization

Industry

IT Services and IT Consulting

Description
Primary skills: Python, SQL, Pandas Secondary skills: Agentic Workflows, Polars, Window Functions Role Overview We are looking for a Python Developer with 4–7 years of experience building data warehousing applications. The role involves developing optimized ETL/ELT pipelines using Python, Pandas, SQL, and preferably Polars. You will work on scalable, high-performance data processing with a focus on parallelism, pipeline optimization, reliability, and analytics-ready datasets. Key Responsibilities • Design, build, and maintain ETL/ELT pipelines for data warehousing. • Develop high-performance data transformations using Python, Pandas, and Polars. • Optimize pipelines for speed, memory usage, and parallel processing. • Write complex SQL/PLSQL queries, including window functions. • Perform data validation, monitoring, and troubleshooting. • Collaborate with cross-functional teams to deliver reliable data solutions. • Document code, workflows, and technical designs. Required Qualifications • 4–7 years of experience in Python or data engineering roles. • 3–4 years of hands-on Python development experience. • Strong knowledge of Pandas; Polars preferred. • Experience building optimized data pipelines and working with parallelism. • 1–2 years of SQL/PLSQL development experience. • Strong understanding of SQL window functions and query optimization. • Experience with testing, debugging, and production support. • Good communication and problem-solving skills.
Responsibilities
Design, build, and maintain high-performance ETL/ELT pipelines for data warehousing. Focus on optimizing data transformations for speed and memory usage while ensuring reliable, analytics-ready datasets.