"When learning meets data, growth becomes measurable and inevitable."

Data Analytics | Data Engineering | AI Systems

Building end-to-end data solutions across ETL, analytics, and machine learning.

Current Project: 🚲 Bike Demand ML System — 4-city Random Forest inference API live on GCP Cloud Run (v4.4.0); RMSE accuracy gates in CI, cost-audit alerting via Slack, Cloud Logging + Prometheus metrics; companion R Shiny dashboard with live GBFS + weather feeds across 6 cities — next: drift monitoring pipeline (v4.5.0)

I'm a data-driven professional passionate about applying AI, Data Engineering and Analytics to improve Business, Learning and Development (L&D) outcomes.

After a successful career in Aviation training and Airport operations, I've transitioned toward data engineering and data analytics, where I can apply analytical methods to solve learning and business problems.

I build data-driven solutions covering:

• AI/ML Engineering — end-to-end training pipelines, inference APIs, and production cloud deployment
• Cloud Data Engineering — GCP Cloud Run, Artifact Registry, BigQuery; containerised CI/CD
• Observability — structured JSON logging (Cloud Logging), Prometheus metrics endpoints
• ETL pipelines and data workflows
• Exploratory data analysis and visualization
• Predictive modelling using Python and R
• Analytics dashboards and reporting systems (R Shiny, Tableau, Looker Studio)

Programming & Analysis:

ML Engineering & APIs:

Visualization & Reporting:

Cloud Data Engineering:

The Sales Data Pipeline evolves from a production-grade Medallion ETL into a full customer analytics platform — unifying transactions, segmentation, and retention into a single source of truth.

flowchart TD
    subgraph Sources["📥 Data Sources"]
        S1["CRM"] & S2["POS / Transactions"] & S3["Web Analytics"]
    end

    subgraph ETL["🏗️ Medallion ETL  ✅  Built — Python · Pandas · Pydantic"]
        B["🥉 Bronze — Ingest + schema validation"]
        C["🥈 Silver — Clean · Dedup · Feature engineering"]
        D["🥇 Gold — Star schema · AOV · CLV pre-aggregated"]
    end

    subgraph Infra["⚙️ Data Infrastructure  🔜  Planned — Airflow · BigQuery · Snowflake"]
        ORC["Apache Airflow<br/>Scheduled DAGs · Dependency tracking"]
        WH["BigQuery / Snowflake<br/>Partitioned · Clustered · Cost-optimised"]
    end

    subgraph Seg["🧠 Customer Segmentation  🔜  Planned — scikit-learn · Databricks"]
        E["RFM Analysis<br/>Recency · Frequency · Monetary"]
        F["Cohort Analysis<br/>Signup cohorts · Engagement lifecycle"]
        G["K-Means Clustering<br/>Unsupervised persona discovery"]
    end

    subgraph Ret["🔁 Retention Analytics  🔜  Planned — scikit-learn · Databricks"]
        H["Churn Classification<br/>At-risk flagging · Re-engagement triggers"]
        I["LTV Correlation<br/>High-value segment identification"]
    end

    subgraph Serving["⚡ Serving Layer  ✅  Built — FastAPI · DuckDB · Docker"]
        J["🦆 DuckDB — In-process analytics"]
        K["FastAPI REST API"]
    end

    subgraph Dash["📊 Analytics Dashboard  🔜  Planned — Tableau · Streamlit"]
        L["KPI tracking · Segment views<br/>Retention curves · LTV by cohort"]
    end

    Sources --> B
    B --> C
    C --> D
    D --> ORC
    ORC --> WH
    D --> J
    WH --> E
    WH --> F
    E --> G
    G --> H
    F --> H
    H --> I
    J --> K
    I --> K
    K --> L

• Google Data Analytics Professional Certificate
• IBM Data Analytics Professional Certificate with Excel & R

My mission is to bridge Data Engineering and Learning — using data to make learning, Business Analysis and training more effective.

📍 Mumbai, India
📧 deepanmehta@live.com
🔗 LinkedIn
💼 GitHub Projects

"When learning meets data, growth becomes measurable and inevitable."