01 / Infrastructure
Project Obsidian
Building a real-time data ingestion engine for a global financial analytics provider.
Data Engineering Core
Build Reliable Data Platforms
Transform raw information into structured, searchable and scalable business assets with our engineering-first methodology.
Critical Friction We Solve
Unstructured data is technical debt. We engineer the systems that liquidate it into intelligence.
Data Silos
Breaking down isolated databases into a unified, high-availability source of truth.
Poor Quality
Automated validation and cleansing layers that ensure data integrity at ingestion.
Manual Reporting
Eliminating spreadsheets with real-time, event-driven observation dashboards.
Disconnected Systems
Orchestrating complex workflows across disparate APIs and legacy environments.
Core Capabilities
Explore Platform SpecsWe Build Robust Foundations
ETL & Data Pipelines
Fault-tolerant orchestration that ingests, transforms, and loads petabytes of data.
#ApacheAirflow#Spark
Knowledge Bases
Vector-optimized stores for RAG and LLM support.
#Pinecone#Milvus
Structured Data
Normalizing chaotic web data into high-schema, queryable relational structures.
#PostgreSQL#DuckDB
Search Infrastructure
Building low-latency full-text and semantic search engines for internal assets.
#ElasticSearch#MeiliSearch
Intelligent Web Crawling
We engineer resilient scraping infrastructures capable of bypassing complex bot detection to harvest market intelligence at scale.
- Dynamic Rendering
- IP Rotation
- Auto-Retry Logic
- Schema Mapping
SELECT global crawls WHERE status active Pinging proxy 192.168.1.1 OK Parsing HTML tree Found 514 nodes Extracting metadata success Ingesting to S3 bucket epiclen-prod-raw
Elite Tech Stack
Airflow
Cloud Native
Engineering Lifecycle
1
Discover
Mapping data sources, schema requirements, and latency SLAs.
2
Architect
Designing resilient pipelines and choosing the right storage engines.
3
Build
Implementation of ETL logic, clean schemas, and robust error handling.
4
Deploy
Containerized deployment with full CI/CD and monitoring integration.
5
Scale
Optimization for throughput growth and predictive system maintenance.
Selected Case Studies
02 / High-Frequency Data
QuantStream
Engineering a sub-millisecond data pipeline for algorithmic trading intelligence.
Technical FAQ
Clarifying our approach to infrastructure and deployment.
Can you handle real-time ingestion from legacy sources?
Yes. We use custom adapters and Change Data Capture mechanisms to stream data from legacy SQL, mainframes, or bespoke APIs without impacting production performance.
What is the typical deployment timeframe?
A standard production-ready MVP pipeline typically takes 4-6 weeks, depending on source complexity and transformation requirements.
How do you ensure data integrity?
We implement validation layers, schema enforcement, and automated unit testing for ETL transformations before they hit the warehouse.