Flexible Part-Time Analytics Specialist

2 weeks ago

palakkad, India beBeeData Full time

About the RoleWe are seeking an accomplished part-time Data Analyst to independently own analytics and reporting work across Power BI, SQL, Google Sheets, and basic data engineering/integration tasks.You will support multiple ongoing client projects by building dashboards, automating data flows, cleaning and modelling real-world datasets, and ensuring that reporting is accurate, reliable, and scalable.This role is ideal for someone who prefers part-time, flexible hours, and enjoys building solutions end-to-end without heavy supervision.Key ResponsibilitiesData Modelling (End-to-End Ownership)Develop dashboards from scratch (data ingestion → cleaning → modelling → DAX → visuals)Use Power Query for data transformation and shapingBuild well-structured, analytics-ready data models (star schema)Write advanced DAX for KPIs, time-intelligence, and business logicConfigure RLS, scheduled refresh, incremental refresh, parameters, etc.Optimise dashboards for performance and accuracySQL (Intermediate to Advanced)Write clean, optimized queries combining multiple datasetsBuild logic for de-duplication, grouping, time-based metrics, trend analysisPerform date/time conversions, timestamp harmonization, and business-rule logicHelp clean and prepare backend datasets for Power BIGoogle Sheets + AutomationWork with operational data stored in Google SheetsUse advanced formulas (QUERY, FILTER, ARRAYFORMULA, REGEX, etc.)Structure Sheets for ingestion into Power BIAutomate repetitive steps using Apps Script (or willingness to learn quickly)Build helper/staging sheets for data cleaning and QAData Engineering Foundations (Lightweight but Practical)Integrate external systems through REST APIs (JSON, tokens, pagination)Use Make.com / Pipedream / Apps Script to automate data ingestionNormalize unstructured SaaS data into clean analytics tablesTroubleshoot data flow breaks and ensure continuity of pipelinesData Cleaning, Structuring & DocumentationClean messy, real-world datasets (duplicates, mismatches, missing fields)Design clear transformation logic from raw data → final modelDocument data definitions, modelling rules, and key assumptionsProvide quality checks to ensure stable reporting

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