Back
Tier-1 brand, popular junior data role, metro location, and broad required skills drive high competition.
Core data engineering skills (Python, SQL, Spark, Airflow) are highly transferable across industries.
Mandatory technical skills (Python, SQL, Snowflake, Airflow, Spark) and CS fundamentals enforce moderate shortlisting strictness.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Collaborate with data scientists and stakeholders to build and productionize scalable ML and data pipelines using tools like Snowflake, Airflow, Spark, and Python.
Design, develop, deploy, and maintain machine learning and deep learning systems from data ingestion to monitoring in both batch and real-time environments.
Own end-to-end solutions including design, code reviews, debugging, and iterative product delivery in a Data Mesh environment focused on engagement marketing and sales forecasting.
Strong fundamentals in computer science: algorithms, data structures, databases, distributed computing.
Proficiency or readiness to master Python, SQL, scientific Python libraries (numpy, pandas), and machine learning/data engineering tools.
B.S. or M.S. in Computer Science, Data Science, Machine Learning, or related experience.
Work Experience Required: Not explicitly mentioned; 1+ years professional software development experience is good to have but not mandatory.
Software engineer with a background or strong interest in data engineering and machine learning implementation.
Experience or willingness to work in complex distributed data environments using production-grade data pipelines and ML model deployment.
Preference for candidates aiming to transition into data engineering from backend development or those comfortable with end-to-end ownership of data and ML solutions.