A selection of recent freelance engagements. Client names are kept confidential where requested. Each case study includes a high-level architecture sketch and the measured outcomes.
Real-Time E-Commerce Analytics Platform#
Industry: E-Commerce · Engagement: ~3 months · Stack: Kafka · Spark Structured Streaming · Databricks · Power BI
Redesigned a batch-based reporting system into a real-time analytics platform for a mid-sized Dutch e-commerce company. The new platform processes over 2 million events per day with sub-minute latency.
flowchart LR src1[Web App]:::src --> kafka src2[Orders DB]:::src --> kafka src3[Inventory]:::src --> kafka src4[Payments]:::src --> kafka kafka[(Apache Kafka)]:::core --> spark[Spark
Structured Streaming]:::core spark --> dbx[(Databricks
Delta Lake)]:::store dbx --> pbi[Power BI
Dashboards]:::out pbi --> users([Business Team]):::user classDef src fill:#1e293b,stroke:#475569,color:#e2e8f0 classDef core fill:#1e40af,stroke:#3b82f6,color:#fff classDef store fill:#155e75,stroke:#06b6d4,color:#fff classDef out fill:#0f766e,stroke:#14b8a6,color:#fff classDef user fill:#3b0764,stroke:#a855f7,color:#fff
Key outcomes:
- Reduced reporting lag from 24 hours to under 60 seconds
- Unified 5 disconnected data sources into a single streaming pipeline
- Enabled real-time inventory and sales dashboards for the business team
LLM-Powered Document Intelligence System#
Industry: Legal / Professional Services · Engagement: ~4 months · Stack: Python · LangChain · OpenAI · PostgreSQL (pgvector) · FastAPI
Built a Retrieval-Augmented Generation (RAG) system that lets legal professionals query large document repositories in natural language. Integrated with the firm’s existing document management workflows.
flowchart LR docs[Legal
Documents]:::src --> emb[Embedding
Pipeline]:::core emb --> pg[(PostgreSQL
+ pgvector)]:::store user([Lawyer]):::user --> api[FastAPI]:::core api --> lc[LangChain
RAG Engine]:::core lc <--> pg lc <--> llm[OpenAI
GPT-4]:::out api --> user classDef src fill:#1e293b,stroke:#475569,color:#e2e8f0 classDef core fill:#1e40af,stroke:#3b82f6,color:#fff classDef store fill:#155e75,stroke:#06b6d4,color:#fff classDef out fill:#0f766e,stroke:#14b8a6,color:#fff classDef user fill:#3b0764,stroke:#a855f7,color:#fff
Key outcomes:
- Reduced document review time by ~60% for participating teams
- Achieved 92% answer accuracy on internal benchmark dataset
- Deployed on Azure with a scalable, cost-efficient architecture
Cloud Data Warehouse Migration#
Industry: Logistics · Engagement: ~6 months · Stack: Python · dbt · Airflow · Snowflake · Terraform
Led the migration of a legacy on-premise Oracle data warehouse to Snowflake for a logistics company. Rebuilt 80+ reports and redesigned the dimensional model from scratch using dbt.
flowchart LR ora[(Oracle
on-prem)]:::legacy --> mig[Migration
Pipeline]:::core mig --> snow[(Snowflake)]:::store snow --> dbt[dbt
Transformations]:::core dbt --> mart[Star Schema
Marts]:::store mart --> rpt[80+ Reports]:::out air[Airflow]:::orch -.orchestrates.-> mig air -.orchestrates.-> dbt tf[Terraform IaC]:::orch -.provisions.-> snow classDef legacy fill:#3f1d1d,stroke:#dc2626,color:#fecaca classDef core fill:#1e40af,stroke:#3b82f6,color:#fff classDef store fill:#155e75,stroke:#06b6d4,color:#fff classDef out fill:#0f766e,stroke:#14b8a6,color:#fff classDef orch fill:#3b0764,stroke:#a855f7,color:#fff
Key outcomes:
- Cut query times by an average of 78%
- Reduced infrastructure cost by 40% year-over-year
- Onboarded analytics team to dbt — full test coverage in 6 weeks
Churn Prediction ML Pipeline#
Industry: SaaS / Fintech · Engagement: ~3 months · Stack: Python · scikit-learn · MLflow · Airflow · AWS SageMaker
Developed an end-to-end churn prediction system for a B2B SaaS company — including automated retraining, drift detection, and integration with the CRM for proactive customer outreach.
flowchart LR src[(Product +
Billing DB)]:::store --> feat[Feature
Engineering]:::core feat --> train[scikit-learn
Training]:::core train --> mlf[(MLflow
Registry)]:::store mlf --> sage[SageMaker
Endpoint]:::core sage --> sf[Salesforce CRM]:::out sf --> csm([Customer Success]):::user air[Airflow]:::orch -.weekly retrain.-> train air -.drift detection.-> sage classDef src fill:#1e293b,stroke:#475569,color:#e2e8f0 classDef core fill:#1e40af,stroke:#3b82f6,color:#fff classDef store fill:#155e75,stroke:#06b6d4,color:#fff classDef out fill:#0f766e,stroke:#14b8a6,color:#fff classDef user fill:#3b0764,stroke:#a855f7,color:#fff classDef orch fill:#7c2d12,stroke:#f97316,color:#fff
Key outcomes:
- Model AUC-ROC: 0.89 on holdout set
- Automated weekly retraining pipeline with data quality gates
- Predictions integrated directly into Salesforce via API
Modern Data Stack Implementation (Startup)#
Industry: Health Tech · Engagement: 6 weeks · Stack: Airbyte · dbt · BigQuery · Metabase · GitHub Actions
Set up a complete data platform from zero for a health tech startup — from raw ingestion to business dashboards — in six weeks.
flowchart LR s1[Stripe]:::src --> ab s2[Postgres]:::src --> ab s3[Salesforce]:::src --> ab s4[12+ other
sources]:::src --> ab ab[Airbyte
Connectors]:::core --> bq[(BigQuery
Warehouse)]:::store bq --> dbt[dbt models
+ tests]:::core dbt --> meta[Metabase
Dashboards]:::out meta --> teams([Product +
Leadership]):::user gh[GitHub Actions]:::orch -.CI/CD.-> dbt classDef src fill:#1e293b,stroke:#475569,color:#e2e8f0 classDef core fill:#1e40af,stroke:#3b82f6,color:#fff classDef store fill:#155e75,stroke:#06b6d4,color:#fff classDef out fill:#0f766e,stroke:#14b8a6,color:#fff classDef user fill:#3b0764,stroke:#a855f7,color:#fff classDef orch fill:#7c2d12,stroke:#f97316,color:#fff
Key outcomes:
- 15+ data sources connected via Airbyte
- Fully documented dbt project with 100% test coverage on critical models
- Self-serve Metabase dashboards adopted by all product and leadership teams
Looking for similar results?#
I’d love to discuss how Demir Technology can help with your data or AI challenge. Get in touch or view the services for engagement details.