Enlight Lab
Snowflake · Databricks · Data Warehouse · dbt

Your Data Platform Is Broken. We Fix It.

Snowflake and Databricks implementations that actually work — pipelines that stay running, clean analytics your team trusts, and a data platform built for your volume, not a demo.

  • Snowflake and Databricks specialists with production experience at real data volumes.
  • Every dbt model documented, version-controlled, and handed over to your team at completion.
  • Pipelines built with monitoring, alerting, and automated recovery — not silent failures.

Clients include

Emblazer.aiHumaMozilla Foundation

5.0on Clutch · CEO, Exar North

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Book a Free Discovery Call

Tell us about your project. We respond same business day.

No obligationNDA on Day 1Same day response

Our clients have raised from & partnered with

Y CombinatorTechstarsGoogleMicrosoftAWSStripeSalesforce

Technology partners & certifications

AWS PartnerGoogle Cloud PartnerMicrosoft PartnerVercel Partner

The Challenges Most Teams Face. And How We Solve Them.

We have seen these problems across hundreds of engagements. Here is where teams consistently get stuck.

Reports Do Not Match the Source Systems

Your dashboards show different numbers than your CRM, ERP, or database. Nobody trusts the data. Meetings start with arguments about which number is correct instead of what to do about it.

How we address itWe audit your data flow end-to-end, identify where numbers diverge, and rebuild the pipeline so reporting matches reality.

Pipelines Fail Without Warning

Data syncs break silently at 3am. Your team wakes up to dashboards showing nothing loaded overnight. Someone manually reruns the job. Nobody knows why it failed.

How we address itWe build pipelines with monitoring, alerting, and automated retry logic. When something fails, you know immediately and the fix runs automatically.

No Documentation or Data Governance

Nobody knows where data comes from, how it is transformed, or whether it can be trusted. New engineers spend weeks reverse-engineering what the existing pipeline does. This is a business risk, not a technical one.

How we address itEvery data model we build is documented in dbt with version control and documented lineage. Your team stops guessing.

Snowflake or Databricks Bills Keep Rising

Warehouse bills are climbing faster than your data usage. Compute credits are wasted on long-running queries that could be a five-minute transformation. Storage grows with no cleanup.

How we address itWe audit your warehouse spend, eliminate wasted compute, implement partition and clustering strategies, and set up cost monitoring so you see exactly where every dollar goes.

The Warehouse Cannot Handle Your Data Volume

Queries that ran fine six months ago are timing out. New data sources have nowhere to go because the schema was never designed for scale. Your data team is spending more time managing the platform than delivering value.

How we address itWe redesign your warehouse schema and pipeline architecture for the volume you have today and where you will be in 18 months.

AI and Analytics Waiting on Clean Data

Your AI features and BI dashboards are only as good as the data feeding them. Machine learning models trained on messy data produce unreliable outputs. Reporting on incomplete pipelines gives misleading signals to leadership.

How we address itWe build the reliable data foundation that makes AI and analytics worth running. Clean data first, insights second.
What You Get

Everything Included. No Hidden Extras.

One engagement, full-stack execution. We own the outcome, not just the deliverables.

01

Snowflake Implementation

Snowflake setup from scratch: schema design, data modelling, cost optimisation, and a transformation layer your analysts can actually use. Built for your data volume, not a generic template.

Fixed-price contractWeekly milestonesLaunch plan
02

Databricks Platform Development

Databricks notebooks, Delta Lake architecture, and Spark pipelines that actually run in production. We build the platform, not just the demo environment.

Technology selection docArchitecture diagramCode documentation
03

dbt Transformation Layer

dbt models that transform raw data into clean, documented, version-controlled datasets. Your analysts write SQL against trusted tables, not raw exports with unknown origins.

Testing suiteProduction deploymentIP transfer
04

Reliable Data Pipelines

Pipeline development with monitoring, alerting, and automated recovery. No more silent failures at 3am or stale dashboards your team cannot trust.

Demo environmentInvestor deck supportLive data integration
100% IP Ownership
All code, designs and IP transferred to you at project completion. No strings attached.
NDA Signed Before Any Discussion
Mutual NDA executed before we discuss any technical details. Your idea is protected from day one.
Senior Engineers Only
No juniors, no outsourcing, no bait-and-switch. The engineers who scope your project build your project.
Engagement Model

Four Phases. Four Weeks.
Every Checkpoint is Working Software.

No status-report theatre. No slide decks. At every phase you receive something you can read, test, or deploy.

1Week 1

Data Audit and Assessment

We map your entire data flow: source systems, pipelines, warehouse setup, and analytics layer. You receive a written assessment: where data is breaking down, which platform (Snowflake or Databricks) fits your workload, and what the right architecture looks like.

Deliverables
  • Data flow map
  • Pipeline audit and risk register
  • Platform recommendation
  • Cost and timeline estimate
2Weeks 2–6

Warehouse and Pipeline Build

We build or rebuild your data platform on Snowflake or Databricks according to the agreed architecture. Every pipeline is monitored, every transformation is documented, and every source system is connected with appropriate retry logic.

Deliverables
  • Snowflake or Databricks warehouse setup
  • Pipelines with monitoring and alerting
  • dbt transformation models
  • Documentation and runbooks
3Weeks 5–6

Validation and Analyst Handoff

We validate data accuracy end-to-end, reconcile numbers against source systems, and hand off to your analytics team with documented, trusted datasets. Analysts write SQL against clean tables instead of debugging raw exports.

Deliverables
  • Data accuracy validation
  • dbt documentation complete
  • Analyst onboarding and SQL walkthrough
  • Cost monitoring dashboard
4As needed

Monitoring and Ongoing Support

We set up alerting and runbooks so your team can operate the platform independently. When you need to add new sources, fix a failing pipeline, or scale the warehouse, we are available on a retainer basis.

Deliverables
  • Monitoring and alerting configured
  • Runbooks for common issues
  • Optional retainer for ongoing support

Ready to start Phase 1?

Free scoping session. Written proposal within 24 hours.

Technology Stack

Enterprise-Grade Tools.
Battle-Tested in Production.

Every technology below has been deployed in production across real client engagements. We choose for longevity and performance, not hype.

Data Platforms
SnowflakeDatabricksBigQueryRedshiftDelta Lake
Transformation & Orchestration
dbtAirflowDagsterFivetranAirbyte
Streaming & CDC
KafkaDebeziumAWS KinesisApache Flink
Analytics & Governance
MetabaseLookerModeAtlanOpenMetadata

Stack selection is driven by project requirements. We advise against over-engineering.

Why EnlightLab

Specific Commitments. Not Marketing Language.

Every firm claims to be reliable, fast, and senior. Here is what those words actually mean in practice when you engage with us.

Snowflake and Databricks Specialists.

We have production experience with Snowflake and Databricks at real data volumes, not just demo environments. We know the difference between a platform that works in a POC and one that works under real load.

Data You Can Actually Trust.

Most data engineering projects fail to deliver because nobody validates the output. We reconcile every pipeline against source systems and hand off only when the numbers actually match.

dbt Native from Day One.

Every transformation we build uses dbt with full documentation, testing, and version control. Your analysts inherit a clean, navigable data model, not undocumented SQL scripts nobody can read.

We Work With Your Existing Team.

Data engineers are expensive and hard to hire. We work alongside your existing team, train them on the systems we build, and leave your team capable of operating independently when the engagement ends.

Snowflake Cost Optimisation Included.

Snowflake bills keep rising faster than your data usage. We audit compute spend, eliminate redundant model runs, implement clustering and partition strategies, and set up cost monitoring from day one.

You Own the Platform.

All pipelines, dbt models, and infrastructure are transferred to your team at completion. We do not build proprietary data platforms or create dependencies that require us to maintain.

Industry Experience

Built for Your Industry

We bring domain context to every project. Our team has delivered across 10 industry verticals.

Healthcare & MedTech
FinTech
Technology & Startups
Education
eCommerce
Real Estate
Travel & Hospitality
Insurance
Renewable Energy
Electric Vehicles
C
5.0
Clutch · Verified Review
Fixed
Price guaranteed
10+
Industry verticals
NDA
Day one

“We had been trying to build a reliable analytics stack for 18 months. Enlight Lab came in, audited everything in a week, and had a working dbt layer in Snowflake within three weeks. For the first time, our analysts trust the data.”

Head of Data
Series B SaaS · Verified Client

“Our Databricks implementation had stalled for months because nobody on the team had built a Delta Lake architecture at our data volume. Enlight Lab scoped it in week one, built it in six weeks, and our ML team was running experiments by week seven.”

VP of Engineering
Growth-Stage SaaS · Verified Client

Testimonials verified via Clutch.co and direct client engagements

Case Studies

Client Outcomes That Speak for Themselves

Real engagements. Real timelines. Real results.

Analytics Infrastructure

Series B SaaS Company

The Challenge

Queries timing out, Snowflake bills doubling every quarter, and analytics teams spending more time debugging data than using it. No clear ownership of the data platform and no documentation of what existed.

Our Solution

Full Snowflake audit and rebuild: eliminated redundant dbt models, implemented clustering and partition strategies, and built a documented transformation layer. Cost monitoring dashboards set up so the team could see exactly where every dollar was going.

40%
Snowflake Cost Reduction
3 Weeks
To Trusted Analytics
0
Query Timeouts

“Our Snowflake bills were doubling every quarter with no explanation. Enlight Lab cut our compute costs by 40% in the first month and gave us full visibility into every credit we were spending.”

VP of Engineering
Growth-Stage Marketplace
Verified client
Databricks & Data Lakehouse

Enterprise Logistics Platform

The Challenge

Legacy Hadoop cluster being decommissioned. Needed to migrate to Databricks, rebuild 40+ pipelines, and implement Delta Lake architecture without interrupting ongoing analytics operations.

Our Solution

Phased migration to Databricks: Delta Lake architecture designed for their data volumes, 40+ pipelines rebuilt in Spark, dbt Core integrated for transformation management, and CDC pipelines from operational databases using Debezium.

40+
Pipelines Migrated
Delta Lake
Architecture
Zero
Downtime

“We were told the migration would take six months and require a full team. Enlight Lab delivered a working Databricks platform with all pipelines migrated in 10 weeks, with zero downtime during the transition.”

Head of Data
Enterprise Logistics
Verified client
Free · No Obligation · NDA on Request

Book a Free
Discovery Call

30 minutes. We review your concept and send a fixed-price proposal within 24 hours of the call.

Fixed price. No surprises.
Your total cost is locked in before we write a single line of code. Invoice matches the quote, always.
Senior engineers from day one.
The engineers on the discovery call build your product. No juniors. No bait-and-switch after you sign.
NDA before any discussion.
Your concept is protected from the first conversation. We sign before you share anything sensitive.
3 Weeks
To Trusted Analytics
<24hr
Proposal After Call
5.0★
Clutch Rating

Book a Free Discovery Call

Tell us about your project and we'll prepare a tailored scope and fixed-price proposal.

Your enquiry is confidential · NDA on Day 1 · We respond same business day

Got Questions?

Frequently Asked Questions

Everything you need to know before booking a call.

Should we use Snowflake or Databricks?
It depends on your workload. Snowflake is the better choice for standard analytics, data warehousing, and BI workloads. Databricks is the better fit for data science, machine learning, and streaming analytics on Lakehouse architecture. We assess your specific workloads during the discovery call and recommend based on what you actually need, not a default preference.
What does a Snowflake or Databricks engagement cost?
Costs vary based on scope and complexity of your existing environment. Most engagements start with a fixed-scope audit and assessment, followed by a scoped build phase. We discuss budget and timeline during the discovery call and propose a structure that fits your situation.
How long does it take to get a data platform up and running?
A working warehouse with connected sources, basic dbt transformations, and validated reporting is typically achievable in three to six weeks, depending on the number of source systems and complexity of the transformations.
We already have a data team. Can you work with them?
Yes. Most engagements augment existing data teams rather than replace them. We help your engineers level up on Snowflake, Databricks, dbt, and pipeline architecture while delivering the core work.
Can you help us reduce our Snowflake or Databricks costs?
Yes. We have cut warehouse spend by 30–50% for several clients by optimising dbt models, eliminating redundant runs, right-sizing compute, and implementing clustering and partition strategies. The cost audit happens in the first week.
How do you handle data quality and testing?
Every dbt model we build includes schema tests, data tests, and column-level documentation. We set up dbt Cloud with CI/CD so that every pipeline change is tested before it runs in production. Your team knows when data breaks and why.
What about dbt? Do you use it?
Yes. Every transformation we build uses dbt with full documentation, testing, and version control. Your analysts inherit a clean, navigable data model. Without dbt, your transformations are undocumented SQL scripts with no lineage. With dbt, your team can actually trust the data.
Who owns the code and infrastructure at the end?
You do. All pipelines, dbt models, and infrastructure are transferred to your team at completion. We do not build proprietary platforms or create dependencies that require us to maintain.
How do I get started?
Book a free 30-minute discovery call. We will ask about your current data environment, your biggest pain points, and what Snowflake or Databricks implementation would actually unlock for your business. Within 24 hours of your call, we will outline a proposed engagement. No obligation.

Still have questions?

Ready to Start Your Project?

30 minutes. No pitch deck. We review your concept, define the scope, and send a fixed-price proposal within 24 hours of the call.

Learn about EnlightLab
NDA on Day 1100% IP OwnershipFixed Price & TimelineSenior Engineers Only