Design, build, test, and maintain backend applications, APIs, and ETL/ELT data pipelines. Perform data transformation, validation, statistical analysis, and optimize code, queries, and pipelines. Troubleshoot production issues, collaborate with cross-functional teams, participate in code reviews, and document architectures and data workflows.
Hybrid Data & Application Developer – “Unicorn Profile”
Experience: 3–5 Years
Job Type: Full-Time
We are looking for a versatile Hybrid Data & Application Developer who can work across both application development and data engineering. The ideal candidate should have strong hands-on experience with Java/JavaScript, Python, and ETL, along with exposure to statistical analysis, data/application optimization, and database technologies.
This role is suited for a “Unicorn” profile — someone who can develop application components while also understanding and working with data pipelines, data transformation, and analytics.
Key Responsibilities- Design, develop, test, and maintain scalable applications and data solutions.
- Develop backend applications, services, and APIs using Java and/or Python.
- Build and enhance application components using JavaScript.
- Design, develop, and maintain ETL/ELT pipelines for data extraction, transformation, and loading.
- Integrate applications with databases, APIs, data platforms, and ETL workflows.
- Perform data transformation, validation, cleansing, and processing using Python and other relevant technologies.
- Analyze application and data-processing workflows to identify performance bottlenecks.
- Perform code, query, application, and data-pipeline optimization to improve performance and scalability.
- Apply statistical analysis techniques to support data-driven solutions and business requirements.
- Develop and optimize SQL queries and database interactions.
- Troubleshoot application, data pipeline, and production issues.
- Collaborate with data engineers, developers, analysts, product teams, and other stakeholders.
- Participate in code reviews and follow best practices for application development, data engineering, security, and maintainability.
- Document application architecture, data workflows, technical solutions, and processes.
- 3–5 years of relevant professional experience in application development, data engineering, or a combination of both.
- Strong hands-on experience with:
- Java / JavaScript
- Python
- ETL / ELT
- Strong understanding of application development and software engineering concepts.
- Experience developing REST APIs and backend services.
- Experience working with databases and writing/optimizing SQL queries.
- Hands-on experience with data extraction, transformation, validation, and loading.
- Understanding of statistical analysis and its application to real-world data problems.
- Experience with application and data optimization, including performance tuning and code/query optimization.
- Strong debugging, analytical, and problem-solving skills.
- Ability to work across both application and data-related requirements.
- SAS experience is an added advantage.
- Experience with frameworks such as Spring/Spring Boot, Django, Flask, or FastAPI.
- Experience with JavaScript frameworks such as React, Angular, or Node.js.
- Experience with cloud platforms such as AWS, Azure, or GCP.
- Exposure to data engineering technologies such as PySpark, Databricks, Airflow, Azure Data Factory, AWS Glue, or similar tools.
- Experience with data warehousing and data modeling.
- Familiarity with CI/CD, Docker, Kubernetes, or other DevOps practices.
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