Role OverviewWe are seeking a Senior Python Data Engineer with exceptional hands-on expertise in Python-based data processing, Pandas, NumPy, and data modeling.
This role is centered around building high-quality data transformation pipelines and designing robust data models that turn raw data into reliable, analytics-ready datasets.
This is a hands-on, individual contributor role for someone who enjoys working deeply with data and writing clean, efficient Python code.
Key Responsibilities- Design and implement data models including analytical, reporting, and transformation-ready schemas.
- Build and maintain data transformation pipelines using Python (Pandas, NumPy).
- Transform raw, semi-structured, and structured data into clean, validated, analytics-ready datasets.
- Apply best practices for data normalization, denormalization, and aggregation.
- Optimize Python data workflows for performance, memory usage, and scalability.
- Implement data validation, reconciliation, and quality checks within transformation pipelines.
- Collaborate with analytics, product, and business teams to understand data requirements.
- Write clean, maintainable, well-documented Python code.
- Review and improve existing transformation logic and data models.
Required Skills & Experience- Strong expertise in Python with heavy, daily use.
- Deep hands-on experience with Pandas and NumPy (non-negotiable).
- Strong understanding of data modeling concepts:
- Fact & dimension modeling
- Star / Snowflake schemas
- Analytical vs transactional models
- Solid experience in data transformation, data wrangling, and feature preparation.
- Good proficiency in SQL and relational databases.
- Experience working with large datasets and complex joins/aggregations.
- Strong problem-solving skills and attention to detail.
Nice to Have - Experience building custom ETL / ELT pipelines in Python.
- Exposure to data quality frameworks or validation techniques.
- Familiarity with Git and collaborative development workflows.
- Experience supporting BI, reporting, or analytics teams.
- Understanding of performance optimization techniques in Pandas (vectorization, chunking, memory tuning).
Experience2 to 4 years of relevant experience in Python data engineering, analytics engineering, or data processing roles.
