Konstantin Sh.Data & AI Engineering
// 01. DATA INFRASTRUCTURE & PLATFORMS|GCP, BIGQUERY, SNOWFLAKE, AWS & PREFECT/KESTRA

Data Solutions & Modern Data Engineering

Transforming raw and fragmented operational data into high-performance analytical warehouses in Google BigQuery, Snowflake, and AWS. Resilient ETL/ELT pipelines orchestrated with Prefect / Kestra and Docker containerized runtimes.

// HOMOLOGATED DATA ECOSYSTEM:
Google Cloud Platform (GCP)Amazon Web Services (AWS)BigQuerySnowflakeETL & ELT PipelinesPrefect / Kestra OrchestrationDimensional ModelingNative SQLDocker ContainersCloud Run & LambdaPythonLooker Studio / BI
// END-TO-END DATA ENGINEERING PIPELINE FLOW[ PROTOCOL: ASYNC ETL & ELT / GCP, AWS & SNOWFLAKE ]
+------------------------------------------------------------------------------------------------------+
| 01. OPERATIONAL DATA INGESTION (OLTP / EXTERNAL APIS / TELEMETRY)                                    |
|  [ Transactional Databases ]    [ Payment Gateways / CRM APIs ]    [ Event Telemetry / IoT Streams ] |
+-------------------------|-------------------------|-------------------------|------------------------+
                          |                         |                         |
                          +-------------------------+-------------------------+
                                                    |
                                                    v
+------------------------------------------------------------------------------------------------------+
| 02. PROCESSING PIPELINES & ORCHESTRATION (PREFECT / KESTRA / DOCKER / CLOUD RUN / AWS LAMBDA)       |
|  - Resilient ETL / ELT batch and streaming workflows orchestrated with Prefect / Kestra              |
|  - Event-driven streaming buffers, automated validation, and retry mechanisms                        |
|  - Strict data contract validation (Pydantic / Pandera) and idempotent loading into DWH              |
+---------------------------------------------------|--------------------------------------------------+
                                                    |
                                                    v
+------------------------------------------------------------------------------------------------------+
| 03. MODERN DATA WAREHOUSE & DIMENSIONAL MODELING (GOOGLE BIGQUERY, AWS & SNOWFLAKE)                 |
|  [ Raw Ingestion (Bronze) ]  ===>  [ Modeled Datasets (Silver) ]  ===>  [ Analytics Datamarts (Gold) ]|
|  - Date partitioning, entity clustering, and Row-Level Security (RLS) governance                     |
+---------------------------------------------------|--------------------------------------------------+
                                                    |
                 +----------------------------------+----------------------------------+
                 |                                  |                                  |
                 v                                  v                                  v
+--------------------------------+ +--------------------------------+ +--------------------------------+
| 04. BUSINESS INTELLIGENCE (BI) | | 05. MACHINE LEARNING & MLOPS   | | 06. AI AGENTS & AUTOMATION     |
| - Looker Studio / Power BI     | | - CatBoost (Demand Forecast)   | | - Pydantic AI / ADK Agents     |
| - C-Suite Executive Dashboards | | - Churn & Inventory Modeling   | | - Automated Ops Workflows      |
| - Single Source of Truth       | | - Continuous MLOps Pipeline    | | - Webhook Actions & CRM Sync   |
+--------------------------------+ +--------------------------------+ +--------------------------------+
//DATA ARCHITECTURE & CAPABILITIES

Four Pillars of Data Solutions

Structured infrastructure engineered to deliver rapid executive insights, centralize disparate business records, and guarantee verified analytical metrics.

// 01. STORAGE & ETL PIPELINESETL + PREFECT/KESTRA + BIGQUERY

Modern Data Warehouse & ETL Pipelines

Centralization and architectural design of enterprise Data Warehouses in Google BigQuery, AWS, and Snowflake. Robust ETL/ELT pipelines orchestrated with Prefect / Kestra, running in lightweight Docker containers with automated retries, observability, and sub-second analytical queries.

>Resilient ETL & ELT pipelines orchestrated with Prefect / Kestra and Docker containers
>Layered modeling architecture (Raw Ingestion, Staging, and Analytical Datamarts)
>Event-driven streaming ingestion, deduplication, and schema contract validation
>Rigorous SQL query cost optimization and slot reservation tuning across GCP & AWS
// 02. PREDICTIVE SCIENCECATBOOST / FORECASTING

Machine Learning & Predictive Models

Development and production deployment of demand forecasting, time-series, and dynamic pricing models utilizing CatBoost, LightGBM, and Python. Pragmatic engineering: models focused squarely on measurable ROI, stock-out prevention, and customer lifetime value optimization.

>Feature engineering with historical sales, calendar seasonality, and market indicators
>Automated retraining pipelines and data drift / model degradation monitoring
>Prediction export directly to ERPs, inventory systems, or operational dashboards
// 03. AGENTS & AUTOMATIONTOOL-CALLING LLMS

Autonomous AI Agents & Tool Calling

Engineering autonomous workflows that connect corporate APIs (CRM, communication platforms, databases, and ERPs) to intelligent agents. We automate complex operational routines such as automated reconciliation, ticket triaging, and proactive anomaly notifications.

>LLM integration with structured function calling and tool use (Pydantic AI / ADK)
>Automated operational alerts triggered by database condition monitors
>Elimination of manual spreadsheet copy-pasting and redundant data entry
// 04. VISUALIZATION & KPISSINGLE SOURCE OF TRUTH

Business Intelligence & Verified Metrics

Direct connection between the Data Warehouse and executive dashboards in Looker Studio, Metabase, or custom Astro/React portals. Focused on establishing the "Single Source of Truth": certified metrics for CAC, LTV, net revenue, and gross margin with zero divergence between departments.

>Pre-computed aggregate tables guaranteeing sub-second dashboard response times
>Role-based access governance and Row-Level Security (RLS) enforcement
>Eradication of isolated, error-prone spreadsheets across management teams
// ENGAGEMENT MODEL FOR DATA ENGINEERING

Technical Contract Allocation Without Hidden Overhead

You don't need to hire a 4-person in-house data department to establish an enterprise-grade Data Warehouse. The engagement is targeted, building the technical foundation scaled specifically to your operational demands.

“Engagement exclusively via technical allocation and hourly contract ($/hr). Bi-weekly invoicing with transparent activity reports. No hidden scopes, no surprise costs.”
Initiate Data Project _>Chat on WhatsAppView Demand Forecasting Case