Data Scientist – Enterprise Reporting & 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• Identify duplicate or outdated reports and centralize metrics.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

Spring, TX | Hybrid | Must Live Within 50 Miles of Spring | Full-Time

iSphere is looking for a Data Scientist who can help a financial services organization get out of the cycle of scattered reports, conflicting numbers, and business users needing IT every time they want an answer.

This is not a pure machine learning role. The focus is much more practical: centralizing enterprise reporting, creating trusted datasets, improving access to data, and giving business users the ability to handle more of their own reporting and analytics.

You will work across data lakes, enterprise data platforms, reporting tools, and business teams to help build a more consistent reporting environment. That includes identifying duplicate or outdated reports, standardizing business metrics, creating reusable datasets and semantic models, and helping move reporting away from spreadsheets and one-off solutions.

A big part of this role is understanding how data moves from source systems into centralized platforms and then into the hands of business users. You should be comfortable working with both the technical side of the data environment and the people who actually need to use it.

What We Need

• Strong experience working with enterprise data and analytics environments
• Hands-on experience with data lakes, enterprise data warehouses, or modern cloud data platforms
• Advanced SQL skills with the ability to work across large and complex datasets
• Experience building self-service reporting and analytics environments
• Experience centralizing or consolidating reporting across multiple departments or systems
• Strong experience with Power BI, Tableau, or similar enterprise BI tools
• Experience creating reusable datasets, data models, semantic layers, or curated reporting structures
• Strong understanding of data quality, data modeling, reporting architecture, and governance
• Ability to gather requirements directly from business stakeholders and turn them into scalable data solutions
• Strong communication skills with the ability to explain technical data concepts without making everyone else regret joining the meeting

Experience in banking or financial services would be a big plus, especially if you have worked with customer, deposit, loan, transaction, risk, finance, or regulatory reporting data.

Experience with Azure, AWS, Snowflake, Databricks, Python, metadata management, data cataloging, or enterprise data modernization will also get our attention.

The client is especially interested in someone who has already lived through this kind of transformation. Maybe reporting was spread across departments, everybody had their own spreadsheet, and three people could produce three different versions of the same number. You helped bring that environment together, create trusted data sources, and give the business better access without creating a new ticket every time someone wanted a report.

If you enjoy turning fragmented data into something people can actually trust and use, this could be a great fit.