Freelancers using Databricks in Lisbon
Freelancers using Databricks in Lisbon
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Devowise Studios
pro
Pakistan
Web Design Studio | Brand Identity | Framer & AI Solutions
$5k+
Earned
2x
Hired
5.0
Rating
106
Followers
expert
Expert
+5
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Web Design Studio | Brand Identity | Framer & AI Solutions
2
Scalable Azure Data Engineering Solution Implementation
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7
1
Clay – Modern Real Estate Website Development
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11
2
NjeyTeam Website Development Project
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9
1
Farmio Agriculture Website Design
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4
Databricks
(1)
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VJ Sojitra
Columbus, USA
AI Engineer | Agentic Systems & Automation | MLOps
New to Contra
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AI Engineer | Agentic Systems & Automation | MLOps
1
I engineered a large-scale package-network intelligence pipeline using Azure Databricks and PySpark. The system joined approximately 4.5 billion historical records and transformed them into reusable fingerprint signals covering package movement and network behavior. I developed time-windowed feature logic using native Spark expressions rather than Python UDFs, preserving distributed performance at scale. The pipeline produced an approximately 200-million-row fingerprint dataset and a 22-million-row operational output that classified package activity along a cold-to-hot temperature scale. The resulting signals supported downstream monitoring, prioritization, and operational analysis. The architecture shown here is a sanitized representation; client identifiers, proprietary schemas, infrastructure paths, and business rules have been removed.
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Last week I shipped a full learning platform: two audience journeys (students + tutors), subscriptions and digital-product sales, lead capture, blog — built solo, end to end.
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I designed and engineered a hierarchical multi-agent orchestration framework for coordinating complex AI-assisted engineering and analytical work. A central orchestrator assigns bounded tasks to specialized builder, analyst, reviewer, and verification agents. Execution follows an explicit dependency graph, while shared evidence, acceptance gates, negative-path testing, and checkpointed state prevent agents from declaring work complete without proof. The framework supports targeted retries, rollback, and recovery from the first incomplete node instead of restarting an entire workflow. It also separates implementation from independent review, reducing the risk of an agent validating its own unsupported conclusions. This independent engineering project demonstrates how agentic systems can be made more controlled, observable, and recoverable. The architecture and control interface are representative views of the framework rather than screenshots containing private execution data.
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I developed an AI-assisted document-intelligence workflow for transportation-agency teams working with large collections of reports, standards, studies, scanned documents, and project records. The system combined document extraction, OCR, structured metadata, semantic retrieval, knowledge relationships, and grounded LLM responses. Analysts could ask questions in natural language, review the supporting evidence, inspect source citations, and approve, revise, or reject generated findings. The workflow was designed around human review, access controls, traceability, and safe failure rather than treating an LLM response as an unquestionable answer. Related work reduced decision time by approximately 60% and improved data accuracy by approximately 35%. The displayed architecture and interface are sanitized representations and contain no client documents, production data, proprietary prompts, or confidential system details.
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117
Databricks
(1)
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Drake Damon
Tampa, USA
Results-driven Data Engineer & Full-Stack Developer building
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Results-driven Data Engineer & Full-Stack Developer building
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Real-time NFL Draft Prediction Model Deployment
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6
0
Real-Time Rental Market Analytics Application
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6
0
Real-Time Marketing Offer System
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5
0
Portfolio
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6
Databricks
(1)
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Carlos Pacheco
Summerville, USA
Talented Data Engineer | Data Architecture
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Talented Data Engineer | Data Architecture
0
Databricks to Terraform Export Transformation
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6
0
301 Moved Permanently
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4
0
Development of DataTrackPro Analytics Platform
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4
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Automic ETL Development for Lakehouse Architecture
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4
Databricks
(1)
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Rajkumar Chatterjee
Kolkata, India
Advisory Consultant: Leveraging Data for Better Decisions
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Advisory Consultant: Leveraging Data for Better Decisions
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Provided independent architecture reviews and second-opinion guidance for leadership teams evaluating complex data and platform decisions, helping surface risks early and validate strategic design choices.
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Senior advisory engagement focused on guiding data and platform strategy for a regulated enterprise, with emphasis on architectural direction and decision-making rather than delivery
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Advisory engagement focused on guiding domain owners and architects on data product and domain-driven architecture principles, helping align data structure, ownership boundaries, and platform design for long-term scalability.
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50
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Fractional architecture leadership engagement supporting solutioning and design decisions for a UK-based asset management firm, providing architectural direction, design reviews, and decision support without owning delivery execution.
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48
Databricks
(2)
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Sreeja .
Bengaluru, India
Data Strategist Building AI-Powered Growth Solutions.
New to Contra
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Data Strategist Building AI-Powered Growth Solutions.
1
:Analyzed Photoshop on iPhone’s 2025 launch impact on Adobe Home, covering CTR, CVR, traffic, orders, and referrals. Found strong engagement but weak conversions, order declines, and trial churn, with recommendations for A/B testing, nurture flows, and benchmarking against Acrobat.
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Skip the License Fee: I Built a Full Dashboard with Just JS Verve Pulse: Revenue & Funnel Intelligence Dashboard Most analysts stop at Power BI or Tableau. I wanted to go a layer deeper by building a fully custom dashboard in React to show what's possible when a BI tool's templates don't box you in. Off-the-shelf visualization tools are great, but they come with real limits: fixed chart types, licensing costs, and UX you can't fully control. With JavaScript, I designed and built Verve Pulse from a blank canvas. Total control over layout, interactivity, and how the story of the data actually reads to stakeholders, and deployed it for free on Vercel, with no licensing cost and no vendor lock-in. The dashboard unifies what's usually three separate reports - current performance, historical trend, and forecast as a single filterable view, with dedicated explorer views for 4 purchase funnels, 4 customer segments, and regional performance, plus a seasonality-adjusted forecast that shows its own assumptions instead of acting like a black box. What I did: Designed the full filter and data framework from a wireframe spec Built a seasonality-adjusted forecasting model (trend decomposition + monthly seasonal indexing) Designed and built the interactive dashboard end-to-end in React. Deployed it as a free, live, secure product on Vercel Why it matters: custom-coded dashboards trade a steeper learning curve for full design freedom, zero licensing cost, and infrastructure you fully control - a combination most Power BI/Tableau builds can't offer. Skills & tools: Data analysis & forecasting · Dashboard/product design · React & Recharts · Git/GitHub · Vercel deployment 🔗 Code: https://github.com/SrijaaBizAnalyst/Revenue-Analytics-Dashboard-for-Verve-Studio
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Verve Pulse is a multi-dimensional revenue analytics dashboard combining current KPIs, 2-year historical trends, and seasonality-adjusted forecasting in one filterable view. Built explorer views for 4 purchase funnels, 4 customer segments, and regional performance — replacing fragmented spreadsheet reporting with a single source of truth. Designed end-to-end from wireframe to interactive React prototype, deployed live on Vercel.
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36
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Revenue Analytics Dashboard for Verve Studio
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2
Databricks
(1)
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Luke Tilja
New York, USA
Enterprise AI strategist building practical automations
New to Contra
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Enterprise AI strategist building practical automations
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Enterprise AI Trend-Intelligence Platform Designed an end-to-end intelligence platform that translated fragmented market and consumer signals into prioritized opportunities. Combined AI strategy, solution architecture, and delivery leadership to create a scalable decision workflow that generated $2M+ in annual savings.
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14
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Predictive Modeling & Decision Intelligence Built a research-grade predictive modeling workflow using XGBoost, point-in-time data controls, validation gates, and calibrated decision logic. The work focuses on reproducible forecasting and auditable model quality rather than unsupported performance claims.
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Multimodal E-Commerce Intelligence System Designed a multimodal product-intelligence concept that connects imagery, catalog data, and semantic reasoning to improve product understanding and decision support. The work combines AI product strategy, solution architecture, and hands-on prototyping for a practical commerce workflow.
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Voice AI Carrier-Rate Negotiation Assistant Designed and built a working prototype that combines conversational voice AI, pricing logic, and workflow automation to support freight-rate negotiations. The system demonstrates how structured guardrails and real-time context can turn a manual call workflow into a repeatable AI-assisted process.
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15
Databricks
(1)
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Saif Al-Musawi
Melbourne VIC, Australia
Sales Analyst | Commercial Growth | Data-Driven | AI
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Sales Analyst | Commercial Growth | Data-Driven | AI
1
Project Title: Enterprise Sales Intelligence & Governance Dashboard (PoC) The Strategy: Before deploying the full-scale governance model, I architected this Proof of Concept (PoC) to validate data-source alignment and core reporting requirements with key stakeholders. The Outcome: This prototype allowed us to stress-test the data model and refine the KPIs, ensuring the final, production-grade dashboard was perfectly aligned with executive decision-making needs.
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Project Title: AI-Driven Sales Intelligence & Workflow Automation The Business Challenge: Manual lead qualification and inconsistent follow-up processes created massive operational bottlenecks, leading to lost conversion opportunities and stalled revenue growth. The Architectural Solution: I architected a closed-loop automation framework to standardize the sales lifecycle. This solution features: Real-time Intelligence: An AI-processing layer that dynamically extracts customer data and matches insurance policy profiles during live interactions. Automated Conversion Pathways: A logic-driven branching model that differentiates between "Ready to Buy" signals and "Needs Time" triggers, ensuring zero lost leads through automated callbacks and personalized SMS sequences. Systemic Optimization: A feedback loop that feeds interaction data back into the performance analytics engine to continuously refine recommendation accuracy and time efficiency. The Impact: Transformed fragmented sales interactions into a high-visibility, data-backed system, directly improving lead recovery rates and operational throughput.
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This piece of work was part of the continuation from the RFP Qualification Agent that I built. This worked on the response given from the other agent once our piece we picked is qualified to understand timelines, dates, and who to get onto the submission of the proposal. Also should mention that it plans what forms or documents we should fill.
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Built an AI-driven RFP qualification agent that automates the evaluation of tender requirements, identifies key risks, and highlights escalation triggers to support faster and more consistent bid/no-bid decisions.
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97
Databricks
(1)
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