Engineering & Software Development

Data Engineer

Reliable analytics and downstream AI features both depend on data infrastructure that ingests, transforms, and stores data correctly and repeatably, the unglamorous plumbing that everything else quietly depends on. We build ETL and ELT pipelines and lakehouse architecture designed for your actual data volume and query patterns, not a generic reference architecture that doesn't fit your scale. This is the service when reporting is slow or wrong because of pipeline issues, or when you're scaling past spreadsheets and need a real warehouse. You get pipelines that are monitored and documented, so a schema change upstream doesn't silently corrupt six months of reports before anyone notices.

This is a capability we deliver for you as a service, not a role we’re hiring for.

How We’d Approach This

A clear, staged plan, not a black box

These are the service-specific steps inside our six-step process.

1
Map current data sources, volumes, and consumer needs to diagnose where the existing pipeline is breaking down.
2
Build a pilot pipeline for one critical data source to validate the transformation logic and warehouse schema.
3
Review data quality and query performance with stakeholders before extending to remaining data sources.
4
Roll out the full pipeline with monitoring, alerting, and documentation for ongoing operation.

What You Get

Deliverables from this engagement

Production ETL/ELT pipelines for your data sources
Warehouse or lakehouse schema documented and query-ready
Data quality monitoring and alerting
Runbook for pipeline operation and troubleshooting

Six Ways We Could Architect This

Different engagement, different build, pick the shape that fits

There’s more than one way to deliver on this service. Browse a few of the ways we’d structure the work, depending on your speed, budget, and integration needs.

Read all 6 architectures as text

Lean Fast-Start Architecture

Data Sources

Application Database
CSV and API Exports

Simple ELT Pipeline

Scheduled Extraction Script
Raw Landing Table
Basic Schema Check on Load
SQL Transformation Layer

Lightweight Warehouse

Single Cloud Data Warehouse
Modeled Tables for Reporting

Consumption and Ops

BI Dashboard
Pipeline Failure Alert Email
Manual Data Quality Spot Check
Documentation Wiki Page

Cloud-Native / Managed Services Architecture

Source Systems

Application Databases
SaaS Tool APIs
Event Streams

Managed Ingestion

Managed ELT Connector Service
Cloud Object Storage Landing Zone

Lakehouse Layer

Bronze Raw Layer
Silver Cleaned Layer
Gold Modeled Layer

Managed Warehouse

Cloud Data Warehouse
Orchestration and Scheduling Service
Data Quality Test Suite

Consumption

BI Tool
Downstream ML Features

Event-Driven & Automation-First Architecture

Triggers

CDC Change Event
File Arrival Event
Scheduled Cron Trigger

Automated Pipeline

Event Driven Ingestion Queue
Transformation Job Runner
Data Quality Test Runner

Orchestration

DAG Orchestrator
Tests Passed
Auto Publish to Warehouse
Auto Quarantine and Rollback
Alert On Call Engineer

Downstream Automation

Trigger Dependent Dashboards Refresh
Trigger Downstream Pipeline Runs

Enterprise-Grade & Compliance-Ready Architecture

Data Governance

Data Source Registry With Classification
Schema Contract Enforcement
PII Tagging and Masking Layer

Controlled Pipeline

Ingestion Service
Transformation With Lineage Tracking
Data Quality Gate

Resilient Warehouse

Primary Warehouse Cluster
Cross Region Replica
Point in Time Recovery Snapshots

Compliance and Audit

Row Level Access Control
Full Lineage and Audit Log
Retention Policy Enforcement
Compliance Reporting Dashboard

AI-Augmented, Human-in-the-Loop Architecture

AI Assisted Monitoring

Incoming Data Batch
ML Based Anomaly Detector
Schema Drift Detector

Automated Diagnosis

AI Root Cause Suggestion
Confidence in Auto Fix

Human Review Gate

Auto Apply Low Risk Fix
Data Engineer Reviews Suggested Fix
Engineer Approves Fix
Weekly Digest of Auto Applied Fixes

Pipeline Continuation

Approved Fix Applied to Pipeline
Pipeline Resumes and Publishes Data
Rejected Suggestion Logged for Model Tuning

Integration-Heavy / Ecosystem-Connected Architecture

Connected Source Systems

CRM
ERP
Product Analytics Platform
Marketing Automation Tool

Central Data Platform

Ingestion and Connector Layer
Transformation Layer
Central Data Warehouse
Data Catalog and Lineage Tool

Ecosystem Consumers

BI and Reporting Tools
Reverse ETL Back to CRM
ML Feature Store
Finance Reporting System

Ready to get started?

Tell us what you’re trying to get done and we’ll help you find the highest-leverage place to start, scoped small enough to prove itself before you commit to anything bigger.

Talk to us about Data Engineer

Most engagements like this start as a $500–$2,500 pilot, see full pricing.