Akhil Kakoju

Data-driven Data Analyst

Talent

HyderabadMember since July 29, 2026

SQLPythonPowerShellMicrosoft FabricCloud Server EcosystemsPower BISource-to-Target MappingChange TraceabilityIntelligent AutomationAzure DevOps

Hire this person

Send a job offer directly to this candidate

SQLPythonPowerShellMicrosoft FabricCloud Server Ecosystems

About

Data-driven Data Analyst with 1.9 years of experience specializing in complex data extraction, pipeline engineering, and automated business intelligence workflows within Agile frameworks.

Certified Microsoft Fabric Data Engineer

Associate skilled in translating raw transactional system metrics into durable, well-governed financial datasets and interactive executive dashboards. Adept at partnering with cross-functional finance and business stakeholders to resolve data definition inconsistencies, perform deep-dive reconciliations, and ensure end-to-end data lineage control.

Experience

Product Engineer - LTIMindtree

(2021-11 - 2023-07)

Project: VISE (Verizon Integrated Solution Engineer)

Data Extraction & Transformation: Spearheaded data extraction and preparation from legacy enterprise customer systems, using advanced Python workflows and complex SQL queries to structure schemas for central reporting.
Source-to-Target Mapping: Documented critical business rules, raw system metrics, and source-to-target mapping configurations to maintain clean data lineage clarity during data infrastructure migrations.
Data Validation & Reconciliation: Automated data consistency checks and orchestrated release pipelines inside Azure DevOps, engineering test validations to investigate variances and resolve dataset inconsistencies across separate systems.
Stakeholder Dashboards: Designed and maintained interactive Power BI business dashboards and automated reports under tight sprint timelines, translating analytical requirements into clear, explainable outputs for non-technical owners.
Governance & Controls: Monitored telemetry across cloud ecosystems, tracking identity datasets, auditing schema updates, and enforcing strict data governance standards and change traceability.
Process Optimization: Leveraged custom Python scripts, PowerShell, and intelligent automation tools to eliminate manual reporting efforts, reducing daily pipeline processing times.