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Senior role with broad ML and full-stack data engineering requirements produces moderate competition among qualified candidates.
Strong technical skills (Python, PySpark, ML) transfer across industries, though analytics domain experience adds specificity.
Explicit 8–15 years plus multiple mandatory technologies (Python, Azure, Spark, SQL) increases shortlisting strictness.
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Design, develop, and deploy machine learning models and analytics solutions primarily using Python and Azure.
Lead and deliver analytics projects across domains such as retail, finance, or consumer analytics, including building dashboards and customer health status analytics.
Refactor legacy Python code, build web applications and APIs, implement automation, and maintain production data ingestion and processing pipelines.
8 to 15 years of professional experience in analytics engineering or related roles.
Bachelor's degree in Computer Science, Data Analytics, or a related field.
Strong expertise in Python including frameworks like Django, Flask or Pyramid, and proficiency in SQL and statistical programming languages like SAS.
Experience with cloud platforms (Azure/AWS), programming and scripting languages (.NET Core, Python, PowerShell, Bash), and building analytics dashboards.
Experienced senior analytics engineer comfortable leading cross-domain machine learning and analytics projects with operational impact.
Proficient in full-cycle analytics and deployment workflows including model building, automation, and production code maintenance, with strong coding and debugging skills.
Able to collaborate effectively with clients and technical teams to translate business needs into analytics solutions and dashboards.