Data Engineering

Platforms That Feed AI and Analytics

Databricks lakehouses, Snowflake warehouses, real-time pipelines with Dynatrace observability. We build data platforms that feed AI, power analytics, and run 24/7 with SLAs. Not architecture diagrams, production code that handles millions of records per second.

  • Cut data latency 30%+ with real-time pipelines
  • Unify scattered data into single source of truth
  • Enable AI-ready data products from day one
  • Instrument observability so you see issues before users

40+ enterprise data platforms deployed | 30%+ average latency reduction

What We Actually Build

We build data platforms on Databricks, Snowflake, and AWS/Azure cloud data services. This isn't abstract architecture, it's production code that handles your data volumes, meets your SLAs, and feeds your AI models.

Most enterprises have data scattered across 10-50 systems accumulated over decades of acquisitions, point solutions, and organic growth. We consolidate that chaos into a governed lakehouse where every dataset has lineage, quality scores, and access controls.

We've done this 40+ times for Fortune 500 companies. When something breaks at 2am, we're on the call with you.

Data Engineering Services

Six core offerings, each delivered with production-grade quality

Unified Data Lakehouse

Consolidate scattered data warehouses into one governed lakehouse built on Databricks or Snowflake.

Key Outcomes
  • 30-40% storage cost reduction
  • Single source of truth
  • AI-ready data products
Azure DatabricksDelta LakeSnowflakeApache Iceberg

Real-Time Data Pipelines

Streaming data pipelines that feed dashboards, ML models, and operational systems with millisecond latency.

Key Outcomes
  • <1 second data latency
  • Real-time insights
  • Self-healing pipelines
KafkaSpark StreamingAWS KinesisAzure Event Hubs

Data Observability & Quality

Monitor data lineage, freshness, and SLAs end-to-end with Dynatrace or similar platforms.

Key Outcomes
  • 90% reduction in quality incidents
  • Full lineage tracking
  • SLA compliance visibility
DynatraceGreat ExpectationsMonte CarloDataHub

DataOps & Automation

CI/CD pipelines for data with automated testing, deployment, and monitoring.

Key Outcomes
  • 40% faster pipeline development
  • Automated testing
  • Version-controlled infrastructure
GitLab CI/CDTerraformAirflowDagster

Data Governance & Cataloging

Enterprise data governance with Unity Catalog, Collibra, or Alation.

Key Outcomes
  • 100% data cataloged
  • Automated PII classification
  • Audit-ready logs
Unity CatalogCollibraAlationApache Atlas

Cloud Data Migration

Migrate on-premises data warehouses to cloud with zero downtime.

Key Outcomes
  • Zero-downtime migration
  • 30-50% cost reduction
  • Legacy decommissioning
AWS DMSAzure Data MigrationSnowflake Migration

Data Engineering Projects We've Built

Real projects. Real Fortune 500 clients. Real outcomes.

MSCIFinancial Services

40+ finance systems post-acquisitions needed consolidation into unified platform

$12MOperational savings in year one
18 monthsDelivery timeline
ZeroFinancial reporting disruptions
SAP S/4HANAPythonAzure DevOps
RaceTracRetail

Payment systems across 600+ locations needed real-time data with zero downtime

30%Reduction in data latency
600+Locations with zero downtime
Real-timeInventory visibility
DatabricksKafkaAWSBraze
SodexoHospitality

Global operations with data scattered across regional silos

SingleSource of truth
GlobalSupply chain visibility
50%Faster decision-making
Informatica IICSMDMCloud Integration

Our Data Engineering Process

From engagement to production: how we work

01

Discovery & Architecture

Weeks 1-4

Assess your current data landscape, understand business requirements, and design the target architecture.

02

Foundation & Setup

Weeks 5-12

Platform provisioning, security framework, governance setup. The foundation everything builds on.

03

Build & Iterate

Months 4-8

Iterative development in 2-week sprints. Build pipelines, transform data, create data products.

04

Launch & Stabilize

Weeks 9-12

Production deployment with monitoring, alerting, and runbooks. We stabilize until SLAs are met.

05

Optimize & Scale

Ongoing

Continuous optimization, cost management, and feature enhancements.

Why Choose ACI for Data Engineering

What makes us different from other consulting firms

Deep Platform Expertise

We're Databricks Exclusive Partner and Snowflake certified with 40+ lakehouse implementations.

40+ enterprise data platforms deployed

Observability Built In

Dynatrace partnership means we instrument observability from day one.

Every platform ships with monitoring

Production-Grade from Start

We don't build pilots that die. We architect for production scale from the first sprint.

Zero production failures in last 3 years

Cost-Effective Delivery

40-60% less than Big 4 consultancies. Senior architects leading, not junior analysts.

70% senior engineers on every project

Common Questions About Data Engineering

How long does a typical data platform project take?
6-12 months for enterprise-scale lakehouse consolidation. Smaller projects (single pipeline, specific integration) can be 3-6 months.
What's the ROI of a modern data platform?
Typical clients see 30-40% reduction in storage costs, 50%+ faster time to insights, and 3-5x improvement in data analyst productivity.
Do we need to migrate everything at once?
No. We use a phased approach, critical systems first, then expand. You'll see value within 3-4 months.
Can you work with our existing cloud provider?
Yes. We're certified on AWS, Azure, and GCP. We design for your environment and can handle multi-cloud.

Ready to Build Your Data Platform?

Schedule a 30-minute technical call with one of our data architects. No sales pitch, just an engineering conversation about your specific data challenges.

Talk to senior data architects, not sales reps|30-minute technical discussion|We'll tell you if we're not the right fit
Talk to a Data Architect