Job Description

ROLES & RESPONSIBILITIES


Qualifications:

Bachelor's degree in Data Science, Statistics, Mathematics, Computer Science, Economics, or a related field. 8+ years of experience in data engineering, with a focus on scalable architecture design. Strong proficiency in Python, PySpark, Python-based UI libraries and Gradio or Streamlit. Strong proficiency in Python, especially in Pandas and NumPy. Strong proficiency in Databricks environment and Native Apps Hands-on experience with Databricks (notebooks, jobs, lakehouse architecture). Experience building charts and dashboards using Matplotlib, Seaborn, or Plotly. Experience in python webapp and rest API development Good to have understanding of SAS (PROC steps, data handling, stored processes). Understanding of data structures, statistical methods, and basic analytical modeling. Good understanding of cloud data architecture and deployment best practices.


JD:

Analyze existing SAS web-based forecasting and reporting applications. Convert SAS stored processes and PROC logic into PySpark equivalent Native Apps. Translate SAS data handlers and preprocessing routines to Python-based scripts. Rewrite forecasting logic and model calculations in Python. Develop interactive dashboards using Python + Matplotlib/Plotly Design and deploy user-friendly Apps for real-time interaction with data and forecasting outputs. Package and deploy the final application within the Databricks App environment. Collaborate with business SMEs to understand requirements and ensure functional parity with legacy SAS systems.
EXPERIENCE


8-11 Years
SKILLS


Primary Skill: Data Engineering
Sub Skill(s): Data Engineering
Additional Skill(s): Python, ETL, databricks, Azure Data Factory, Pyspark