Data Engineering & Warehousing
Clean data. Confident decisions.
We build data pipelines, warehouses, and lakehouse architectures that transform fragmented raw data into a verified single source of truth — ready for analytics, AI training, and compliance.
Engineering mobile systems built for scale.
AI and analytics are only as valuable as the data they run on. Most businesses have data spread across CRM, ERP, payment systems, product databases, and marketing platforms — with no unified, trustworthy view. We build the data infrastructure that changes that: reliable pipelines, a governed warehouse, and the tooling that makes data genuinely self-service.
Built for high-growth companies and operational teams.
Companies with data fragmented across 3+ systems
Analytics or data science teams bottlenecked by data quality
Businesses preparing for AI training data requirements
Organizations with compliance data retention obligations
Enterprise-grade mobile architecture capabilities.
Data Pipeline & ETL/ELT Engineering
Batch and streaming data pipeline engineering
ETL and ELT workflow design with dbt transformation layers
API-to-warehouse ingestion pipeline development
CDC (change data capture) integration for real-time sync
Pipeline orchestration with Airflow or Prefect
Data Warehouse & Lakehouse Architecture
Snowflake, BigQuery, and Redshift warehouse design and implementation
Data lakehouse architecture on AWS S3 / GCS with Delta Lake
Dimensional modeling, data vault, and schema-on-read design
Cost optimization and query performance engineering
Data Quality & Observability
Data quality testing frameworks (Great Expectations, dbt tests)
Data lineage tracking and documentation
Pipeline monitoring, alerting, and SLA enforcement
Schema change management and backward compatibility systems
Real-Time Streaming Pipelines
Kafka event streaming pipeline engineering
Flink and Spark Structured Streaming processing
Real-time aggregation and materialized view systems
Event schema registry and data contract management
Problems we solve at the infrastructure level.
Fragmented data across multiple disconnected systems
No unified data layer means every analysis requires manual aggregation — producing different answers depending on who runs the report.
Analysts blocked by data quality issues
Teams spend the majority of their time cleaning data rather than analyzing it — a symptom of missing pipeline governance.
AI initiatives blocked by poor data infrastructure
ML models and AI systems require reliable, well-structured training data — which doesn't exist without proper data engineering foundations.
Performance metrics that impact business growth.
Pipeline reliability
Query performance improvement
Data freshness
Analyst time on cleaning vs analysis
Real-world deployment and measurable outcomes.
E-commerce data platform
Unified 6 data sources into a single Snowflake warehouse — real-time revenue visibility, query time reduced from 8 min to 40 sec.
FinTech streaming pipeline
Kafka pipeline processing 2M+ daily events with <3 second latency — enabled real-time fraud detection.
Modern engineering stack optimized for scale.
Trusted across operationally demanding industries.
Build scalable digital products engineered for long-term growth.
Partner with Santi IT Farm to engineer high-performance mobile systems, scalable infrastructure, and enterprise-grade digital experiences.