PP - Software Engineer with Data B. - Job4777 at Taller Technologies

, , -

Full Time

Start Date

Immediate

Expiry Date

06 May, 26

Salary

0.0

Posted On

05 Feb, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, PySpark, Pytest, Terraform, Software Engineering, Data Ingestion, Data Transformation, Automated Testing, SQL, Fintech, Banking, System Design, Infrastructure As Code, ETL, Credit Products, Data Quality

Industry

IT Services and IT Consulting

Description

PP - Software Engineer with Data B. - Job4777 Summary We are seeking a Senior Python Software Engineer to join the Credit Platform Data team. This role is strongly focused on software development and engineering best practices, with data as a domain rather than the primary discipline. You will be responsible for designing and building a scalable data ingestion and transformation framework that enables internal teams to inject data, apply standardized transformations, and generate reliable reports and dashboards. This includes not only building robust backend systems, but also ensuring correctness and quality through comprehensive automated testing across data pipelines, reporting layers, and dashboards. This role requires deep expertise in Python, PySpark, and Pytest, strong Terraform knowledge, and experience working in fintech or banking environments, where data accuracy, auditability, and reliability are critical. Responsibilities Design and build Python-based software frameworks for data ingestion, transformation, and reporting, following solid software engineering principles. Develop reusable, maintainable, and well-tested core libraries and services that standardize how data is injected, processed, and exposed. Implement scalable data processing systems using Python and PySpark, focusing on performance, reliability, and extensibility. Collaborate in the creation of data transformation layers that feed analytical reports and dashboards, ensuring consistency and correctness of outputs. Write comprehensive automated tests using Pytest, covering: Core framework logic, Data transformations, Data quality validations, and Generated reports and dashboards. Implement automated data quality checks and validation processes to ensure data integrity at scale. Build and maintain SQL scripts and database integrations to support data workflows. Use Infrastructure as Code (Terraform) to provision and manage cloud resources. Monitor system health, troubleshoot production issues, and continuously improve performance and stability. Create and maintain design documents, runbooks, and technical documentation for data systems. Participate in design reviews and code reviews, promoting best practices in software engineering and testing. Collaborate with product managers, analysts, and stakeholders to define requirements and deliverable timelines. Requirements Bachelor’s degree in Computer Science, Engineering, or a related field. 5+ years of experience as a Software Engineer, with a strong focus on backend or platform development. Strong software engineering background, including system design, clean architecture, and maintainable codebases. Proficiency in Python, including building frameworks and shared libraries. Hands-on experience with Terraform or similar Infrastructure as Code tools. Strong experience with PySpark for large-scale data processing. Strong experience with Pytest and automated testing strategies for complex systems. Solid SQL knowledge and experience working with relational databases. Deep understanding of ETL concepts and data extraction, transformation, and loading processes. Fintech experience is a must-have, including familiarity with banking concepts such as: Credit products, Financial transactions, Regulatory requirements, Data accuracy and reconciliation. Experience working in Linux/Unix environments and shell scripting. Strong analytical, problem-solving, and communication skills. Ability to work collaboratively across engineering, data, and business teams.

Responsibilities

Design and build Python-based software frameworks for data ingestion, transformation, and reporting, focusing on reusable, well-tested core libraries and scalable processing systems using PySpark. Implement comprehensive automated testing using Pytest across pipelines and reports, and manage cloud resources via Terraform.