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# Business Data Analyst/ Architect

- **Company:** Diverse Lynx LLC
- **Location:** Princeton, NJ, United States
- **Experience:** Expert
- **Salary:** $104,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Data Analysis, Confluence, JIRA, Big Data, Data Architecture, Data Dictionary, Data Integration, Data Presentation, Data Profiling, Apache Hadoop, Python (Programming Language), Knowledge Management, PostgreSQL, Machine Learning, NumPy, Reference Data, Microsoft Copilot, SQL Databases, Feature Engineering, Sql Optimization, Large Language Models, Snowflake, Prompt Engineering, Apache Spark, Data Strategy, Git, Pandas, Data Lineage, Collibra, Data Analytics, Bitbucket, Data Management, Tools for Reporting, Software Version Control, Data Pipelines, Amazon Redshift, Databricks
- **Published:** October 29, 2026
- **Apply:** https://www.thejobnetwork.com/job/7bb595c9-d54c-4c26-92f9-67e6b57741f7/business-data-analyst-architect

## About the Role

" 10 years of experience in data analytics, business analysis, or data
management.
" Advanced SQL expertise with ability to query and Eligible to workimize large, complex
datasets.
" Proficiency in Python for data analysis, transformation, and feature engineering
(Pandas, NumPy).
" Experience with building dashboard using AI or similar visualization
frameworks for data storytelling and dashboards.
" Deep understanding of data architecture, governance, MDM, and data
lineage.
" Proven track record in identifying and scoping new data use cases and
converting insights into business recommendations.
" Strong analytical thinking, stakeholder management, and communication skills.
" Basic working knowledge of AI/ML concepts and exposure to AI-assisted tools
(e.g., Copilot-style coding assistants, LLM-based chat/analytics tools, or prompt
engineering for data tasks) no deep ML modeling expertise required.
" Experience with Snowflake, Databricks, or Hadoop/Spark environments.
" Exposure to data catalog or governance tools (Collibra, Alation).
" Familiarity with D&B data hierarchy, enrichment, or similar reference data
systems.
Environment & Tooling Stack
" SQL (PostgreSQL, Snowflake, Redshift)
" Python (Pandas, NumPy, feature engineering)
" AI for visualization
" Git / Bitbucket for version control
" JIRA / Confluence for project tracking and documentation

## Description

" Data Analysis & Insight Generation: Perform in-depth analysis of enterprise
data to uncover trends, patterns, and correlations. Identify actionable insights
and data-driven opportunities that inform decision-making and strategic
initiatives.
" Data Use Case Identification: Collaborate with stakeholders to identify and
define data use cases across functional domains, ensuring alignment with
business priorities and future-state data strategy.
" Business-to-Technical Translation: Engage with stakeholders to translate
business requirements into clear technical specifications, analytical models, and
actionable deliverables.
" Impact Assessment: Analyze current-state data and infrastructure versus
future-state goals; identify data and process gaps; define mitigation strategies.
" Data Profiling & Quality Analysis: Perform deep dives into structured and
semi-structured datasets to assess completeness, consistency, and accuracy;
work with SMEs to understand data availability and source gaps.
" Data Integration & Transformation: Design and validate data pipelines and
transformations; document lineage and dependencies.
" Feature Engineering & Analytics: Use Python and SQL to extract, clean, and
engineer data features for analytics and modeling.
" AI-Assisted Analytics: Apply basic AI/ML and LLM-based tools (e.g., prompt-
based data exploration, AI-assisted feature engineering, or generative AI
copilots) to accelerate data analysis, documentation, and insight generation.
" Reporting & Visualization: Build and maintain insightful dashboards and
reports to communicate findings effectively.
" Governance & Documentation: Maintain business glossary, data dictionary,
and metadata aligned with governance standards.
" Testing & Validation: Participate in validation of new or transformed data sets to
ensure integrity and alignment with business requirements.
" Knowledge Management: Maintain the data dictionary and business glossary
for assigned domains, capturing and documenting definitions aligned with
business leadership and global teams.
" Cross-functional Collaboration: Partner closely with data engineers, product
managers, and business SMEs to align solutions with business objectives.