Tiger Analytics

AI Engineer

• Own the end-to-end architecture of the Snowflake Cortex RAG pipeline, aligned with Enterprise TPRM Standards
• Act as the primary technical liaison to client business owners, AI leads, and InfoSec SMEs
• Lead UAT sessions with InfoSec SMEs and convert feedback into prioritized technical backlog items
• Prepare the Model Development Documentation packet and drive AIEC-2 clearance before production deployment
• Direct daily agile standups and sprint planning for the three-person offshore pod
• Ensure milestone delivery within the SOW budget

Lead AI Architect, Delivery Manager

Business Intelligence Developer

• Design, develop, and deliver clear, intuitive, and interactive Power BI dashboards
• Translate complex datasets into actionable stakeholder insights
• Author and optimize complex DAX measures, calculations, and custom time-intelligence logic
• Evaluate key business metrics and organizational KPIs
• Connect, extract, and transform structured and unstructured data from diverse enterprise sources
• Use Power Query (M) and automated data flows
• Diagnose, troubleshoot, and optimize report performance
• Tune DAX measures, refine data models, reduce cardinalities, and leverage star-schema architecture
• Implement and manage governance and report security protocols, including Row-Level Security (RLS) and Object-Level Security (OLS)
• Collaborate with data engineers, analysts, data scientists, AI teams, business leaders, functional teams, and cross-disciplinary stakeholders
• Translate analytical questions into impactful visual reporting solutions
• Support analytics, machine learning, and Generative AI initiatives across the organization

Lead Power BI Developer

Manager

• Engage with business stakeholders to gather and formulate requirements and develop solution constructs for enterprise BI and dashboard initiatives
• Analyze business requirements and construct conceptual models and data flow processes
• Communicate data architecture to development teams
• Conduct design reviews, including wireframing and prototyping, to ensure business needs are met
• Use business intelligence tools to build and support dashboards
• Partner with team members and cross-functional partners to support business processes and system solutions
• Manage and resolve client issue escalations and systemic improvements
• Work with offshore teams to develop and deliver BI dashboards and reports and bring scalable solutions to business stakeholders
• Lead validation efforts to ensure data quality
• Identify and execute risk mitigation and compliance exercises

Manager – BI Consulting, Power BI

Business Analyst

• Lead requirements gathering, analysis, and documentation for system enhancements and upgrades
• Define sub-domain scope and entity lists, including business definition, grain, and priority
• Map business processes to source systems and establish the system of record per entity
• Scan Databricks / Snowflake catalogues with data engineering leads and produce entity-to-source-to-dataset mappings with coverage verdicts
• Lead SME interviews to elicit use cases, capabilities, and attribute-level requirements
• Build Discovery readouts and secure client sign-off
• Own BRD/FRD documentation and canonical attribute registers with source arbitration and traceability from use case to attribute
• Act as functional authority for data engineers on business rules, grain, hierarchies, and code sets
• Review STTMs against signed requirements and assess change requests for scope impact
• Define UAT scope and business scenarios traced to requirements
• Run UAT with business SMEs, triage defects, drive issues to closure with evidence, and secure UAT sign-off
• Document residual open items and handover material

Business Systems Analyst – Healthcare

Client Partner

• Own overall responsibility for delivery, account management, client relationships, and account growth.
• Engage with CXO, VP, and Director-level stakeholders to understand strategic business priorities and demonstrate business value.
• Develop analytics roadmaps by identifying key business questions, challenges, and high-value use cases.
• Scale pilots and POCs into long-term, transformative analytics programs.
• Collaborate with sales leaders to develop POVs, proposals, and solution approaches supporting new business and account expansion.
• Apply advanced data science and analytics solutions to solve complex business challenges in the Retail & CPG domain.
• Lead and mentor data and analytics professionals while working closely with client stakeholders.
• Partner with globally distributed teams to translate client business needs into effective solution and delivery strategies.
• Own client presentations, project governance, expectation setting, and overall relationship management.
• Drive continuous value realization and identify opportunities for account growth.

Client Partner – Retail, CPG

Data Scientist

• Extract decades of EMPRV data, including work orders, asset hierarchy, maintenance history, and materials data, likely from Oracle, into Delta tables
• Perform maintenance and work-order analytics, including backlog and aging, planned versus actual labor and cost, repeat work orders, crew productivity, and schedule adherence
• Develop reliability and asset-health models covering failure patterns, time-to-failure, survival models, risk-based maintenance prioritization, and anomaly detection
• Conduct materials and inventory analytics, including spare-parts demand forecasting, slow-moving or obsolete stock analysis, and bill-of-materials consumption analysis
• Build a Databricks App as a self-service front end to replace legacy EMPRV queries and reports
• Apply text analytics and LLMs to technician work-order notes to classify failure modes or cause codes
• Drive advanced analytics initiatives focused on improving operations analytics accuracy
• Collaborate directly with operations stakeholders to define data based on how EMPRV is used

Senior Data Scientist – Operation Analyst

Data Engineer

• Design, build, and maintain scalable ETL/ELT pipelines
• Architect and manage secure enterprise cloud storage
• Ingest and process high-volume batch data and real-time streaming workloads
• Optimize database queries, PySpark jobs, and data pipeline performance
• Collaborate with BI and analytics teams to model dimensional data marts for reporting
• Implement data governance, automated data quality validation, and end-to-end lineage tracking
• Secure data pipelines and storage using Azure RBAC, Key Vaults, and encryption standards
• Automate infrastructure deployments and code releases using Azure DevOps, GitHub Actions, and CI/CD best practices
• Collaborate with data scientists, analysts, and AI teams to support analytics, machine learning, and Generative AI initiatives

Senior Data Engineer

Data Analyst

• Break down complex, ambiguous business problems into structured analytical approaches and propose innovative, technically sound solutions
• Drive solution discovery with stakeholders by clarifying requirements, assessing analytical feasibility, and shaping technical roadmaps
• Independently scope, lead, and deliver analytical workstreams with minimal guidance
• Ensure timely, high-quality analytical outputs
• Deliver insights for revenue growth, customer patterns, and operational efficiencies
• Translate complex data insights into actionable recommendations for technical and non-technical stakeholders and business leaders
• Build, optimize, and deploy predictive and machine learning models
• Lead analytics workstreams and collaborate with cross-functional teams
• Apply financial and analytical judgment to identify opportunities and act decisively with incomplete information

Manager / Senior Manager, Principal Data Analytics

• Engage business stakeholders to understand objectives, economics, processes, constraints and decisions
• Challenge assumptions and convert ambiguous business issues into clear hypotheses, analyses and decision frameworks
• Explore and interpret data, conduct rapid analyses, identify patterns and translate findings into business implications and recommendations
• Use GenAI, Agentic AI, analytics and low-code/no-code tools to explore ideas, analyze information and develop working prototypes
• Leverage data scientists, engineers, domain experts, product teams and AI agents when deeper expertise is required
• Communicate findings in simple business language, recommend actions and translate recommendations into measurable business outcomes
• Experiment with emerging AI and technology capabilities to improve business decision-making