Business Intelligence Analyst

Logic Software Solutions•Stamford, CT

2d•Hybrid

About The Position

The Business Intelligence Analyst will help design, build, and deploy intelligent workflows that transform multimodal data (structured, semi-structured, and unstructured) into verifiable, transparent, and compliant insights. This role blends strong analytical thinking with practical AI application, grounded in rigor, accountability, and measurable business impact. The analyst will collaborate with business stakeholders to identify operational inefficiencies and design AI-driven automations, architect and implement intelligent automations using BI methods and LLMs, extract, clean, and integrate data, maintain documentation for compliance, communicate AI concepts to stakeholders, and stay updated on emerging AI tools.

Requirements

Master’s degree in Data Science, Decision Science, Industrial/Organizational (I/O) Psychology, Computer Science, or a related quantitative field.
4+ years in a data analyst, business analyst, technical product, or BI engineering role.
Expert-level proficiency in SQL: writing, debugging, and optimizing complex queries (joins, window functions, CTEs, query performance tuning) across relational databases (e.g., PostgreSQL, MySQL, or Snowflake).
Experience using Python for data profiling, cleaning, and analysis, including libraries such as Pandas, NumPy, and Jupyter notebooks.
Hands-on experience applying foundation models (e.g., GPT-4, Claude, Llama) to real business problems, including prompt engineering, chain-of-thought reasoning, retrieval-augmented generation (RAG), and working with LLM APIs (OpenAI, Anthropic, or similar).
Strong analytical foundations (statistics, hypothesis testing, data visualization principles).
Proven ability to identify, diagnose, and resolve data quality and integrity issues.
Ability to explain AI-driven concepts to both technical and non-technical audiences.
Demonstrated bias toward action, intellectual curiosity, and disciplined execution in fast-paced, evolving environments.
Commitment to the highest ethical standards, particularly regarding data privacy, compliance, and responsible AI use.

Nice To Haves

Hands-on experience with SQL (PostgreSQL, MySQL, Snowflake, BigQuery), NoSQL (Elasticsearch, MongoDB).
Experience with Python (Pandas, NumPy, Scikit-learn, Hugging Face, LangChain), Jupyter, Git.
Experience with BI & Visualization tools like Tableau, Power BI, Looker, or Superset.
Experience with AI/LLM Tooling such as OpenAI API, Anthropic API, LangChain, LlamaIndex, vector databases (Pinecone, Weaviate, FAISS), basic RAG patterns.
Experience with Workflow & Automation tools like Apache Airflow, Prefect, or similar orchestration tools; basic API integration (REST).
Experience with Documentation & Compliance tools like Markdown, Jupyter notebooks, data catalog tools (e.g., DataHub, Amundsen).
Experience with Cloud & Data Platforms such as AWS (S3, Lambda, Redshift), GCP, Azure, Databricks, or Snowflake.

Responsibilities

Collaborate directly with business stakeholders (e.g., compliance, operations, legal, trading desks) to identify operational inefficiencies and design AI-driven automations that meaningfully improve day-to-day workflows.
Architect and implement intelligent automations using a combination of traditional business intelligence (BI) methods and modern large language models (LLMs).
Translate complex business problems into technical solutions with clear success metrics.
Extract, clean, and integrate data from multiple internal and external sources (databases, APIs, logs, document repositories).
Build reusable data pipelines that feed both analytical dashboards and automated decision systems.
Maintain thorough documentation of data lineage, testing protocols, and audit trails.
Ensure all outputs (reports, models, alerts) are verifiable, reproducible, and meet firm-wide accuracy and regulatory compliance standards.
Serve as a knowledgeable bridge between business teams and engineering/product partners.
Help non-technical stakeholders understand AI capabilities, limitations, and value propositions.
Present findings and recommendations to senior management.
Monitor emerging AI tools, agentic frameworks, and prompt engineering techniques.
Proactively identify practical opportunities for adoption to improve speed, quality, and efficiency across the organization.
Diagnose and resolve data quality issues, including inconsistencies, missing values, and anomalies.
Implement automated validation checks and monitoring.