Oracle Fusion Technical Consultant
Version 1 is seeking a Senior Oracle Fusion Technical Consultant to design and develop custom extensions and applications for Oracle Fusion Cloud. The role involves utilizing Oracle VBCS, APEX, and OCI to build scalable solutions that enhance ERP and HCM functionality. You will lead technical workstreams, collaborate with cross-functional teams, and ensure adherence to security and governance standards. The position offers a clear career path into architecture roles and involves work on major digital transformation programs.
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Experience
8+ years
Function
Engineering
Work mode
Hybrid, India
Company
Tier 2
What you will work on
Version 1 is seeking a Senior Oracle Fusion Technical Consultant to design and develop custom extensions and applications for Oracle Fusion Cloud. The role involves utilizing Oracle VBCS, APEX, and OCI to build scalable solutions that enhance ERP and HCM functionality. You will lead technical workstreams, collaborate with cross-functional teams, and ensure adherence to security and governance standards. The position offers a clear career path into architecture roles and involves work on major digital transformation programs.
TAL's take
Solid tier-2 consulting firm role offering specialized Oracle Cloud development work, though lack of stated compensation limits the score.
The JD is extremely well-defined, with clear technical stack requirements, specific Oracle modules mentioned, and clear expectations for the role's function.
Salaries at Version 1
29.0 LPA average
Based on 2 Grapevine salary entries for Version 1.
Other roles
8 - 10 years
18 LPA average
Range: 18 - 18 LPA
Other roles
12 - 14 years
40 LPA average
Range: 40 - 40 LPA
Must haves
- 8+ years of Oracle Cloud or PaaS development experience
- 3+ years in VBCS and/or APEX
- Experience delivering custom extensions for Oracle Fusion ERP and HCM Cloud
- Expertise in VBCS application design and REST API consumption
- Hands-on experience with Oracle APEX on ATP/ADW or DBCS
- Proficiency with Fusion SaaS integration methods like FBDI, HDL, and BICC
Tools and skills
Nice to have: oracle process automation, oracle integration cloud, oracle fusion data intelligence, oracle analytics cloud.
About the company
Established international IT services and consulting firm with 3000+ employees, significant revenue, and a strong history of partnership with major technology vendors.
Posts mentioning Version 1
Grapevine January 13th update
Thank you for all the love and support over the last few weeks. :) Grapevine is just 5 weeks old and we are a very small team :) But we are trying address feature requests as they come from you all. A few things from us: 1. We recommend you update the app from the play/app store for the best experience 2. As per popular demand - timestamps have now been introduced in the app (available on latest version) 3. New (and hopefully better) algo for the popular feed has been rolled out Lastly, please feel free to drop in your feedback and feature requests in this post. We will try to implement everything we can.
Title: Data Scientist (~2 YOE) – Built planning tools, pipelines, and AI system. Need honest feedback on profile.
Hi all, Looking for practical feedback on my profile before I start applying. I’ll keep this structured so it’s easier to evaluate. --- 1) Planning Tools / Web Applications Problem: Forecasting workflows were fragmented and heavily Excel-driven: - Multiple data sources (orders, shipments, different forecast versions) - Manual merging, lookups, and adjustments - No way to simulate scenarios or compare forecasts cleanly - Different planners using different methods → inconsistency What I built: - Two internal applications for planning workflows: - A planning tool integrating 8+ data sources - A forecasting simulator supporting multi-level editing (high → granular) Key capabilities: - Real-time scenario simulation - Side-by-side comparison of multiple forecast types - Hierarchical adjustments across levels - SQL write-back for persistence Scale: - Processes ~150K+ records per cycle - Used in monthly planning cycles by multiple teams Impact: - Removed fragmented Excel workflows - Enabled consistent decision-making across users - Reduced manual effort and improved visibility into forecast behavior --- 2) Automation & Data Pipelines Problem: Core workflows were manual and repetitive: - Multi-file Excel processing - Data cleaning + merging across systems - Version tracking errors - High effort per cycle (1–4 hours depending on workflow) What I built: - Multiple pipelines automating end-to-end workflows Examples: - Large-scale consolidation pipeline: - Input: ~1M+ rows across 20+ files - Output: clean, unified dataset (~75% reduction) - 2nd pipeline: - Replaced a 23-step manual process - Standardized inconsistent formats across datasets - 3rd automation processing: - Automated unpivoting, enrichment, and version tracking Impact: - Reduced processing time from hours → minutes per cycle - Eliminated manual errors (copy-paste, lookup mistakes) - Standardized workflows across users --- 3) Power BI / Monitoring Problem: Recent data (orders/shipments) showed inconsistencies, but: - No visibility into changes over time - Hard to identify where data drift was happening What I built: - Power BI dashboards with: - Hierarchical filters - Drill-down views - Month-over-month comparison Scale: - ~30K+ records analyzed Impact: - Enabled early detection of data inconsistencies - Helped planners validate inputs before forecasting - Improved trust in upstream data --- 4) Side Project (AI System) What I built: - AI-powered job assistant system Features: - Scrapes job postings - Scores relevance using LLMs - Generates tailored resume points and outreach messages - Tracks applications Tech: - FastAPI backend - LLM routing (cloud + local fallback) - SQLite storage Goal: - Build a system-driven workflow (not just model usage) --- My concern Most of my work sits at the intersection of: - forecasting - data systems - workflow automation I’m trying to move into: 👉 Applied Data Scientist / Product-oriented roles --- Questions 1. Does this profile look too niche (forecasting-heavy)? 2. Does “building systems around data” help or hurt for DS roles? 3. What’s the biggest gap you see (if any)? Would really appreciate honest feedback. Thanks.
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