Lead Software Engineer

Data Analyst – Data & BI TransformationRole Overview

An experienced Data Analyst to support the organization's data and analytics transformation

initiative across Databricks, AWS S3, Oracle, Aurora, Power BI and related data platforms.

The Data Analyst will work closely with business stakeholders, product owners, data engineers,

BI engineers and architects to understand business requirements, translate them into analytical

and reporting requirements, analyze data, define business metrics, validate data quality, and

deliver actionable insights through Power BI.

The role will operate within a SAFe Agile delivery environment and requires strong

collaboration across Agile teams, business stakeholders and technology teams.

The ideal candidate combines business analysis, data analysis, SQL, data visualization and

data quality skills, with an understanding of modern cloud data architectures.

Key Responsibilities1. Business & Data Requirements

• Work with business stakeholders and Product Owners to understand business

processes, analytical needs and reporting requirements.

• Translate business requirements into clear data, analytical and visualization

requirements.

• Facilitate requirements workshops and stakeholder discussions.
• Define business rules, calculations, KPIs and reporting logic.
• Develop user stories, acceptance criteria and analytical requirements within the SAFe

Agile framework.

• Identify opportunities to simplify and standardize reporting requirements.
• Work with stakeholders to establish common definitions for critical business metrics.
• Data Analysis & Exploration
• Analyze data across Databricks, AWS S3, Oracle and Aurora to identify trends,

patterns, anomalies and business insights.

• Write complex SQL queries to extract, transform and analyze data.
• Perform exploratory data analysis and investigate business questions.
• Analyze large and complex datasets across multiple source systems.
• Identify relationships and dependencies between data sources.
• Investigate discrepancies between systems and reports.
• Support root-cause analysis of data and business issues.
• Data Discovery & Understanding
• Work with Data Engineers and Architects to understand enterprise data models and data

pipelines.