Python Developer - Data Engineering & Analytics


Job Summary:

We are seeking a highly skilled and experienced Python Developer to join our Data Engineering & Analytics team. You will play a key role in designing, developing, and maintaining robust data pipelines, APIs, and data processing workflows. You will work closely with data analysts and business teams to understand data requirements and deliver insightful data-driven solutions. The ideal candidate is passionate about data, enjoys problem-solving, and thrives in a collaborative environment. Experience in the financial or banking domain is a plus.

 

Responsibilities:

Design, develop, and maintain robust and scalable data pipelines using Python, SQL, PySpark, and streaming technologies like Kafka.
Perform efficient data extraction, transformation, and loading (ETL) for large volumes of data from diverse data providers, ensuring data quality and integrity.
Build and maintain RESTful APIs and microservices to support seamless data access and transformation workflows.
Develop reusable components, libraries, and frameworks to automate data processing workflows, optimizing for performance and efficiency.
Apply statistical analysis techniques to uncover trends, patterns, and actionable business insights from data.
Implement comprehensive data quality checks and perform root cause analysis on data anomalies, ensuring data accuracy and reliability.
Collaborate effectively with data analysts, business stakeholders, and other engineering teams to understand data requirements and translate them into technical solutions.


Qualifications:

Bachelor's or Master's degree in Computer Science, Data Science, Information Systems, or a related field.
5+ years of proven experience in Python development, with a strong focus on data handling, processing, and analysis.
Extensive experience building and maintaining RESTful APIs and working with microservices architectures.
Proficiency in building and managing data pipelines using APIs, ETL tools, and Kafka.
Solid understanding and practical application of statistical analysis methods for business decision-making.
Hands-on experience with PySpark for large-scale distributed data processing.
Strong SQL skills for querying, manipulating, and optimizing relational database operations.
Deep understanding of data cleaning, preprocessing, and validation techniques.
Knowledge of data governance, security, and compliance standards is highly desirable. Experience in the financial services industry is a plus.
Familiarity with basic machine learning (ML) concepts and experience preparing data for ML models is a plus.
Strong analytical, debugging, problem-solving, and communication skills.
Ability to work both independently and collaboratively within a team environment.


Preferred Skills:

Experience with CI/CD tools and Git-based version control.
Experience in the financial or banking domain.