Enterprise Reporting Data Scientist: Self-Service Analytics

iSphere Global; The Woodlands (TX); Hybrid; USD 100,000 - 140,000; Full time

Job summary

iSphere is seeking a Data Scientist to help a financial services client consolidate reporting, clean data, and empower business users to self-serve analytics. This is not pure ML; focus is on centralizing datasets, standardizing metrics, and building semantic layers.

You'll work across data lakes, warehouses, and BI tools, translating business requirements into scalable data solutions while collaborating with stakeholders to reduce spreadsheet chaos and improve data trust.

Qualifications

• Strong experience handling enterprise data and analytics environments.
• Hands-on use of data lakes, data warehouses, or modern cloud platforms.
• Advanced SQL skills across large, complex datasets.
• Experience building self-service reporting and analytics.
• Ability to consolidate reporting across departments or systems.
• Proficiency with Power BI or Tableau and other BI tools.
• Ability to design reusable datasets, data models, and semantic layers.
• Solid understanding of data quality, modeling, governance, and reporting architecture.
• Ability to translate business requirements into scalable data solutions.

Responsibilities

• Work across data lakes, enterprise data platforms, and BI tools to standardize reporting.
• Identify duplicate or outdated reports and centralize metrics.
• Create reusable datasets and semantic models for self-service analytics.
• Understand data movement from source systems to centralized platforms.
• Collaborate with business stakeholders to gather requirements and deliver scalable solutions.

Skills

Enterprise data

SQL

Self-service analytics

BI tools

Data modeling

Data governance

Stakeholder communication

Data lake / data warehouse

Tools

Power BI

Tableau

Azure

Snowflake

Databricks

Python

Job description

iSphere is seeking a Data Scientist to help a financial services client consolidate reporting, clean data, and empower business users to self-serve analytics. This is not pure ML; focus is on centralizing datasets, standardizing metrics, and building semantic layers.

You'll work across data lakes, warehouses, and BI tools, translating business requirements into scalable data solutions while collaborating with stakeholders to reduce spreadsheet chaos and improve data trust.