Head of Data & Digital · Tbilisi, Georgia
Archil
Chachanidze
Head of Data & Digital at Silk Development. Building greenfield data infrastructure across 49 SAP B1 instances and 1,800 Bitrix tables, feeding an AI BI agent layer for natural-language analytics over both.
The throughline across everything I build: enterprise AI agents are about to access data at a scale the existing audit and privacy tooling can't see. piifind, agent-ledger, and Silk's gold-layer governance work are pieces of that missing layer.
7+ years
Data engineering experience
99% uptime
From a 40% baseline in 8 months
100+
Legacy jobs migrated to managed workflows
Currently building
See all →
01
Experience
Apr 2026 – Present
Silk Development
Head of Data & Digital Apr 2026 – Present
- Too early for concrete outcomes here. See ongoing work for what's currently in flight: SAP B1 multi-company warehouse, Bitrix denormalization pipeline, and an AI-BI-agent surface over both.
Dec 2021 – Nov 2025
Making Science · Sweeft
Senior Data Engineer Nov 2024 – Nov 2025
Promoted after stabilizing a business-critical data platform and reducing failures from daily incidents to roughly one per quarter.
- Improved a key pipeline from 40% uptime to 99% within 8 months while keeping production live during the rebuild.
- Re-architected ETL orchestration for GA4, Adobe Analytics, and CM360 using BigQuery, Airflow, Cloud Functions, and Pub/Sub, reducing latency and restoring SLA performance.
- Established Python and Airflow implementation standards and onboarded new engineers into the platform.
Data Engineer Dec 2021 – Nov 2024 · 3 yrs
- Built and supported large-scale ETL/ELT pipelines for Adobe Analytics, GA4, CM360, and Power BI exports on GCP.
- Migrated 100+ legacy scripts into Airflow and Cloud Scheduler with retries, monitoring, and SLA controls.
- Introduced Pydantic validation and schema normalization to reduce drift and improve downstream data quality.
- Automated BigQuery loads for Looker Studio so analysts had fresher reporting data without manual requests.
Oct 2020 – Dec 2021
Olmait
Python Developer
Built real-time ETL services between vision hardware and robotics control systems.
- Delivered backend and data integration services on Cloud Functions and Cloud Run.
- Built REST APIs to automate data movement between internal systems and third-party platforms.
Feb 2020 – Oct 2020
Freelance
Freelance Data Engineer
- Delivered Python automation, data extraction, API connectors, and lightweight ETL for e-commerce and analytics clients.
- Managed Linux deployments, backups, and recurring scheduled data jobs.
Jul 2019 – Nov 2019
Birtvi
DevOps Engineer
- Operated Kubernetes and Docker infrastructure for a trading platform and supported production deployments.
- Resolved operational issues across running services and release workflows.
Oct 2018 – Jul 2019
Birtvi
Python Programmer
Built multithreaded trading infrastructure across multiple live exchange APIs.
- Developed real-time price feed ingestion, order execution, and backtesting workflows.
- Supported production trading systems running across multiple exchanges.
Jul 2016 – Sep 2017
Newtelco Georgia
System Administrator
- Managed network infrastructure, servers, and internal IT support.
02
Selected Projects
Chaos-injectable synthetic data engine on FastAPI. Generates deterministic, YAML-schema-defined data, then injects controlled failures so the systems consuming it can be tested against realistic chaos.
- 19 chaos operations: schema drift, latency, encoding corruption, late arrivals, partial loads, header anomalies, and more.
- Cross-schema correlation: the same
customer_idstays consistent across orders, payments, and events via Redis-backed pools. - Pre-generation worker fills a Redis queue so WebSocket streams pop pre-built records for low-latency delivery, with live generation as fallback.
- LLM authoring agents turn natural-language prompts into valid schemas and chaos profiles via multi-step tool calling (LiteLLM-backed).
- Pre-1.0: built for dev / CI / staging; stress-tests ETL jobs, stream consumers, dashboards, and alerting rules.
7,278items/sec peak (single host)
19chaos operations
30-cellreproducible bench matrix
Marketing Analytics Platform Redesign
Redesigned analytics infrastructure for three teams with different source systems, consolidating ingestion into contract-driven Airflow pipelines on GCP. Result: 99% uptime, 1M+ rows processed per day, and one production failure across a year of operation.
99%uptime
1M+rows/day
50production DAGs
Additional delivery
- Historical data normalization - Unified 100GB+ of legacy CSV data in BigQuery.
- FTP pipeline rebuild - Replaced a manual process with automated ETL on Cloud Functions.
- Adobe Analytics ETL - Automated end-to-end ingestion with standardized output schemas.
- CM360 conversion pipeline - Built rule-based CSV processing with automated weekly delivery.
- Robotics pipeline - Delivered real-time ETL from vision hardware into robotics control and history storage.
- Cloud ETL microservices - Built REST and Selenium-backed services on Cloud Run.
- Trading bot - Developed a multi-exchange real-time execution engine with Dockerized services.
03
Technical Skills
Core Stack
Python
production ETL pipelines and data services
SQL
data modeling and query performance tuning
BigQuery
partitioned tables, analytics workloads, large-scale querying
Apache Airflow
orchestration, scheduling, retries, and SLA management
ETL / ELT
idempotent and schema-aware pipeline design
FastAPI
data APIs and contract-first service development
GCP Services
Pub/Sub
event-driven ingestion and decoupled workflows
Cloud Functions
serverless ETL and lightweight automation
Cloud Run
containerized services and scheduled workloads
GCS
staging, archival, and data lake storage
Data Engineering Practices
Data Contracts
Pydantic validation and schema drift control
Pipeline Architecture
modular, configuration-driven systems
Idempotent Processing
safe reruns, replayability, and operational reliability
Storage, APIs, and Delivery
PostgreSQL
relational modeling and transactional systems
Redis
caching, stateful workflows, and fast lookups
Docker
reproducible services and containerized pipelines
Pandas
profiling, transformation, and exploratory analysis
Infrastructure
Terraform
GCP provisioning and environment setup
CI/CD
GitHub Actions and automated deployment workflows
Kubernetes
production operations and service deployment
Prometheus + Grafana
monitoring, alerting, and service visibility
Working Knowledge
Apache Spark
familiar with local development and core concepts
dbt
comfortable with transformation patterns
Kafka
tested locally and understand event-streaming basics
Snowflake
querying and analytical usage
Databricks
notebooks and platform fundamentals
AWS / Azure
working familiarity outside primary GCP experience
04