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Senior Analytics Engineer-BI-EIIIJ

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Hybrid

Senior Analytics Engineer-BI-EIIIJ

Bounteous

Senior Analytics Engineer-BI-EIIIJ

Skills

SQL QueriesData EngineeringPythonData ScienceNumPypandasSnowflakeData AnalysisMachine LearningWarehouse Management

1 month ago

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Important information


Contract type:

Permanent

Location:

Bengaluru, Karnataka, India

Work mode:

Hybrid

Published on:

25 September 2026

What they need


Hello Everyone,

Greetings from Accolite Digital India Private Limited!!

We have an excellent Opening for the below Skills:

Job Title: Senior Analytics Engineer

Experience: 5 to 9 Yrs

Max Notice: Immediate-2 Weeks

Primary Skills:

  • Snowflake
  • SQL
  • Python
  • Pandas
  • NumPy
  • Data Quality
  • Statistics
  • Hypothesis Testing
  • Regression
  • Feature Engineering
  • EDA
  • Machine Learning

Job Location: Chennai,Bangalore,Hyderabad

Interested Candidate Kindly share the updated Profile to [email protected] along with the below details,

Current CTC:

Expected CTC:

Available Time to Join:

Preferred Location:

JD:

Job Title: Senior Analytics / Data Science Engineer

About the Role

We are looking for a Senior Analytics / Data Science Engineer who sits at the intersection of data engineering and data science. In this role, you will build and own the data infrastructure that powers analytics and machine learning workflows ensuring data is accurate, performant, and ready for insight. You will work closely with data science, engineering, and business teams to deliver production-grade solutions that drive real decisions.

What You'll Bring

You have 5+ years of experience in analytics engineering, data engineering, or an equivalent technical role, with a track record of owning outcomes rather than just completing tasks.

On the technical side, you bring strong, hands-on SQL skills and deep Snowflake experience including performance tuning, warehouse management, and a genuine awareness of cost implications. You are comfortable with Python for data work, including pandas, NumPy, scripting, and writing tests. You have built and maintained data accuracy and quality frameworks, not just flagged issues.

You also have a solid foundation in statistics — distributions, sampling, hypothesis testing, regression, and confidence intervals — and a working knowledge of the data science workflow: exploratory analysis, feature engineering, and model evaluation metrics. You don't need to be a full ML engineer, but you understand the pipeline well enough to support and improve it.

What We Value

Beyond technical depth, we are looking for someone who takes ownership, communicates proactively, and brings a quality mindset to everything they ship. If something looks off in the data, you investigate it. If a process can be improved, you improve it.

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