Data Engineering
Data pipelines that don't break when your business scales.
18 years of enterprise data means we've inherited every broken pipeline — and learned how to build ones that don't break.
ETL/ELT pipelines, data warehouses, lakehouses, and streaming architectures designed to handle 10x your current volume without rebuilding. Data quality, lineage tracking, and monitoring built into every pipeline from day one.
Data quality is infrastructure
Your analysts should be able to trust the numbers.
Data quality enforced at ingestion is orders of magnitude cheaper than data quality enforced at consumption. We build validation, monitoring, and lineage tracking into every pipeline — so your analysts never have to question whether the numbers are right.
cost difference: fixing data at source vs. at consumption
Industry benchmarks
Data contracts
Schema, freshness SLA, and completeness requirements defined between every producer and consumer.
Ingestion validation
Validation rules at every ingestion point — rejecting bad data before it propagates downstream.
Lineage tracking
Full lineage from source to dashboard — trace any number back to its origin in minutes.
Freshness monitoring
Real-time alerts when data is stale, incomplete, or violating its quality contract.
How we build data infrastructure
From audit to production — a process designed to avoid the mistakes we've seen 18 years of.
Audit existing infrastructure
Document every pipeline, data store, and transformation in scope. Most organisations have more data infrastructure than they think — and more broken infrastructure than they know. Building on undocumented foundations produces undocumentable outcomes.
Define data contracts
Before writing a pipeline, define the contract between producer and consumer — schema, freshness SLA, completeness requirements, and what happens when the contract is violated.
Build with quality at the source
Pipelines built with validation at every ingestion point, incremental processing for efficiency, and schema evolution handling. Designed to handle 10x current volume without architectural changes.
Instrument for observability
Row count checks, schema validation, freshness monitoring, and lineage tracking. When something breaks, the team traces the failure to its source in minutes, not hours.
Data engineering capabilities
From batch pipelines to real-time streaming architectures.
Reliable, scalable ETL/ELT for structured and semi-structured data.
Version-controlled, tested SQL transformations with documentation and lineage built in.
Fivetran, Airbyte, or custom connectors for SaaS, databases, APIs, and file-based sources.
Airflow or Dagster pipelines with dependency management, retry logic, and monitoring.
Built for scale
Test for 10x before you need it.
A pipeline that handles today's volume may not handle 10x volume at all. We load test pipelines before go-live and design the scaling strategy — horizontal partitioning, incremental processing, or streaming — before the data arrives. The architecture decisions that matter at scale are different from the ones that matter at launch. We plan for both.
events/day at peak
headroom designed in
AurvikAI data engineering — observability built into every pipeline from day one.
Inherited infrastructure vs. AurvikAI-built infrastructure
The difference between data infrastructure your team avoids and data infrastructure your team trusts.
What we usually inherit
- Undocumented pipelines nobody dares touch
- Data quality issues discovered by executives in board meetings
- No lineage — nobody knows where a number came from
- Fragile cron jobs that fail silently every weekend
- Temporary data stores that became permanent 3 years ago
What we build
- Documented pipelines with data contracts and ownership
- Quality validated at ingestion — bad data never propagates
- Full lineage from source to dashboard in minutes
- Orchestrated pipelines with monitoring, alerting, and retry logic
- Architecture designed for 10x scale with clear upgrade paths
INSIGHTS
Thinking worth reading
Ready to build data infrastructure that scales?
Let's start with an audit of what you have — and a plan for what you need.