Headline

Analytics Engineer & Data Consultant | dbt, Snowflake, SQL, Python | Automated ELT Pipelines | Power BI (DAX) | 3+ YOE

About

Data Professional with 3+ years of experience designing scalable data architectures, automated ELT workflows, and high-performance reporting layers. Specialized in transforming raw operational data into analytical schemas using dbt Core, Snowflake, and SQL Server/PostgreSQL. Proficient in building dimensional models (Star Schemas / Medallion Architecture), optimizing query execution plans across 1M+ row datasets, and delivering executive Power BI solutions with custom DAX. Core Technical Stack: • Data Modeling & Transformation: dbt Core, Dimensional Modeling (Kimball), Star Schema, Medallion Architecture (Bronze/Silver/Gold) • Warehousing & Databases: Snowflake, PostgreSQL, SQL Server, Query Optimization, Partitioning & Indexing • Programming & Pipeline Automation: Python (Pandas/NumPy), Linux CLI/WSL, Git/GitHub, REST API Ingestion • Business Intelligence: Power BI (DAX, Power Query), Data Storytelling, KPI Scorecards Open to Analytics Engineer and Data Analytics Consulting opportunities.

Location

Bangalore Urban

AI stack

claude code

codex

gemini

github copilot

Microsoft Copilot

Work experience

Data Analytics Consultant at Maa Kali Recovery Service, SNR & SPS Automobiles, Jul 2023 - Aug 2026 · 3 yrs 2 mos, Baleshwar, • Processed 100+ daily operational records exported from Tally and Excel across 3 business units, standardizing schemas and eliminating entry errors prior to centralized database ingestion. • Structured and loaded cleaned transactional data into a SQL Server database, cutting daily operational consolidation time from 2.5 hours down to under 30 minutes. • Wrote SQL queries using CTEs, window functions, and aggregations to generate weekly reports tracking total revenue, net margins, and top 10 best-selling parts across 500+ active inventory SKUs. • Built weekly accounts receivable tracking models to isolate overdue customer balances, stabilizing average credit settlement cycles within 14 to 30 days. • Designed monthly Power BI dashboards featuring dynamic DAX KPIs to track month-over-month (MoM) revenue growth, category-wise profit margins, and paid vs. unpaid transaction ratios. • Analyzed parts turnover trends to identify 25+ slow-moving salvage components, enabling owners to clear dead-stock and prioritize high-demand inventory

Education

BCA, Computer Science, Bhadrak Autonomous College

Public links

github: https://github.com/Manas-Sensei