Data Application Developer (R / Python)
Department: Data and AI
Experience: 2-3 Years
Job Summary
Key Responsibilities
- Design, develop, and maintain data applications using R (Shiny preferred) or Python frameworks.
- Build interactive dashboards and custom user inputs with a focus on performance and usability.
- Implement non-blocking / asynchronous user operations in Shiny or equivalent frameworks.
- Develop and maintain data pipelines for ingestion, transformation, and storage of structured and unstructured data.
- Use Git for version control, code reviews, and collaborative development workflows.
- Develop efficient data manipulation workflows using packages such as dplyr, data.table, pandas, NumPy, etc.
- Write optimized SQL queries and work with large-scale relational databases.
- Handle and process large datasets efficiently and reliably.
- Collaborate with product and business teams to translate requirements into technical solutions.
- Assist in integrating LLM/AI capabilities into data tools and internal applications.
- (Optional) Build reports or integrations using Power BI or similar BI tools.
- Ensure high code quality, documentation, testing, and maintainability.
Required Skills & Qualifications
- 2–3 years of professional experience in data application development, analytics engineering, or a similar role.
- Strong proficiency in R or Python (R is highly preferred).
- Hands-on experience with R / Python Shiny or equivalent Python frameworks (e.g., Streamlit, Dash, FastAPI).
- Solid understanding of data manipulation libraries/packages.
- Strong knowledge of SQL and experience working with complex queries.
- Expertise in writing complex SQL queries, including multi-table JOINs, window functions, CTEs (including recursive), and subqueries (correlated and non-correlated).
- Strong experience in SQL performance optimization and large-scale data handling, including use of indexes, execution plans, and working with partitioned tables and high-volume datasets.
- Descriptive statistics (mean, median, variance, standard deviation, percentiles)
- Probability distributions (normal, binomial, Poisson)
- Hypothesis testing (t-tests, chi-square tests, ANOVA)
- Confidence intervals and statistical significance
- Correlation and basic regression analysis
- Interpreting statistical results for business decision-making
- Proven experience working with large datasets.
- Good understanding of event-driven / non-blocking application design.
- Experience building custom UI components and user inputs in Shiny or similar frameworks.
- Strong working knowledge of Git (branching strategies, pull requests, code reviews).
- Experience designing and maintaining data pipelines (ETL/ELT workflows).
- Basic working knowledge of LLMs / AI integration, such as using APIs (OpenAI, HuggingFace, etc.), prompt design, or embedding AI features into applications.
Preferred / Nice to Have
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