Senior Data and Analytics Engineer

Pure Storage

8 - 12 years

12 - 16 Lacs

bengaluru

Posted:14 hours ago| Platform:

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Skills Required

snowflake python sap c data analytics modeling oracle financial systems pyspark data warehousing aiml data engineering business intelligence artificial intelligence sql data quality tableau data modeling spark fpa predictive modeling finance ml

Work Mode

Work from Office

Job Type

Full Time

Job Description

Design, build, and own the
Finance Data Cube
, integrating financial, customer, and operational datasets.
Model data to support
CLTV, Contribution Margin
, and other profitability KPIs across multiple dimensions (customer, product, LOB, geography, etc.)
Be a data architect for Finance , bring different data into cohesive and answer critical questions
Leverage
AI tools
to automate data summarization, anomaly detection, and narrative insights for Finance stakeholders.
Scale data infrastructure to support large volumes and complex financial models.
Translate complex financial concepts and KPIs into
scalable, maintainable data models.
Implement strong controls for
data quality, lineage, and governance.
Maintain clear documentation of data transformations, metric definitions, and AI model logic.
We are primarily an in-office environment and therefore, you will be expected to work from the Bangalore office in compliance with Pure s policies, unless you are on PTO, or work travel, or other approved leave.

WHAT YOU BRING

Experience:
8-12 years in
Finance Data Engineering, FP&A Analytics, or Financial Systems
, with exposure to
AI/ML-based analytics
(added advantage)
Proficiency with
Snowflake
and modern data stacks with strong SQl and data modeling expertise
Deep understanding of finance data set , SAP , C
ontribution margin, CLTV
would be an added advantage
Experience with
data orchestration tools
(Airflow, dbt) and tableau platform
Hands-on experience with
Python, PySpark, or R
for automation and model integration.
Proven ability to translate
financial logic into scalable data models
.
Knowledge of
predictive modeling, time-series forecasting, and generative AI
in analytics.
Ability to
evaluate and apply AI responsibly
in financial contexts.

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