Data engineering & analytics services

We build the data infrastructure that turns raw records into trusted answers.

Deep Data Company specializes in warehousing, pipelines, access governance, and the BI layer that sits on top of it — for teams who need their data to be correct, secure, and fast to query.

SourceWarehouseSecuredBI
medallion layers row / column access

What we do

We turn scattered, unreliable data into infrastructure your business can act on

Every dashboard, model, and decision is only as good as the data underneath it. We design and operate the layer most companies never see — warehouses that hold up, pipelines that don't fail silently, access rules that hold under audit, and dashboards people actually trust — so your team spends time acting on data, not arguing about whose number is right.

What we do

Core service areas

Click a tile for the in-depth breakdown of what's included.

Platforms

Tools we work in

Platform-agnostic by design — the right tool depends on what your team already runs on.

Looker Tableau Domo Power BI Pentaho

How we work

Built to be handed off, not babysat

Every engagement ends with something your team can maintain — not a black box only we understand.

Audit first

Before building anything, we map what exists — sources, current pipelines, who touches what — so the solution fits your reality, not a generic template.

Document as we go

Access rules, pipeline logic, and dashboard sources are documented as they're built, not reconstructed afterward.

Hand off cleanly

Your team gets a walkthrough and clear ownership of every layer — warehouse, pipeline, security rules, and dashboards.

Have a data problem worth solving properly?

Tell us what you're working with — current stack, main pain point, and rough timeline — and we'll follow up with next steps.

Email
info@deepdatacompany.com
Based in
Remote — available for global engagements
Typical engagement
2–8 weeks, scoped per project

Data Warehousing

Modeling data so it stays queryable as it grows

Raw data from operational systems is rarely fit to query directly. This is the layer where it gets structured, cleaned, and organized into something reliable — the foundation everything else in the pipeline depends on.

Data Warehouse ETL Development

We design extract, transform, and load workflows that pull from your source systems — databases, APIs, flat files — and land clean, structured data in the warehouse. Built for incremental loads, schema drift, and repeatable runs, not one-off scripts.

  • Incremental and full-load pipelines
  • Schema change handling
  • Data quality validation at each load
  • Connectors for databases, APIs, and flat files

Medallion Architecture

Data is organized into bronze, silver, and gold layers — raw, cleaned, and business-ready — so every transformation is traceable and reprocessing never means starting over from the source.

  • Bronze layer: raw, immutable ingestion
  • Silver layer: cleaned and conformed data
  • Gold layer: aggregated, business-ready tables
  • Clear lineage between every layer
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Data Engineering

Moving data on schedule, with someone watching

A warehouse is only useful if data actually arrives in it, on time, in the right order. This is the layer that moves it there and tells you the moment something breaks.

Data Pipeline Building

End-to-end pipelines connecting your source systems to the warehouse, whether that's a nightly batch job or a near-real-time stream. Every pipeline is idempotent, so a re-run never duplicates or corrupts data.

  • Batch and streaming pipelines
  • Idempotent, safely re-runnable jobs
  • Built-in error handling and retries
  • Documented source-to-target mappings

Data Orchestration

Jobs rarely run in isolation — one depends on another finishing first. We set up scheduling that respects those dependencies, so downstream tables never build on incomplete upstream data.

  • DAG-based scheduling
  • Cross-system dependency management
  • Backfill and re-run support
  • SLA monitoring on critical jobs

Data Alert Mechanisms

When a pipeline fails or the data itself looks wrong — a row count drops to zero, a value falls outside its normal range — you hear about it before your stakeholders do, not after.

  • Pipeline failure alerts
  • Data anomaly detection
  • Threshold-based notifications
  • Delivered to Slack, email, or on-call tools
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Data Security & Governance

The right people see the right data — nothing more

Before data reaches a dashboard, access rules decide who can see what. This layer keeps sensitive fields and restricted rows enforced consistently, everywhere the data is queried.

Row-Level Security

Users see only the records relevant to them — a regional manager sees their region, a rep sees their own accounts — enforced at the query level, not by filtering in the dashboard.

  • Region and role-based row filtering
  • Dynamic policies tied to user attributes
  • Consistent enforcement across every BI tool

Column-Level Security

Sensitive fields — salaries, personal identifiers, contract terms — stay masked or hidden from anyone without explicit clearance, while the rest of the table remains fully usable.

  • Masking of PII and other sensitive fields
  • Field-level permission sets
  • Audit-ready access logs

Group-Based Access Control

Permissions are managed by team or group rather than one rule per person, so onboarding a new hire — or revoking access when someone leaves — takes one change, not a dozen.

  • Role and group hierarchies
  • Centralized permission management
  • Faster onboarding and offboarding
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Business Intelligence

Where the data finally reaches people

All of the modeling, movement, and access control exists to support this: systems that talk to each other, and dashboards that people actually open every day.

Data Integration

We connect the systems that would otherwise stay siloed — CRM, ERP, product analytics, spreadsheets someone still updates by hand — so information reconciles instead of contradicting itself across tools.

  • API and database connectors
  • Real-time and batch synchronization
  • Master data reconciliation across systems

BI Dashboard Creation

Dashboards built in whichever tool fits your stack — Looker, Tableau, Domo, Power BI, or Pentaho — designed around the questions each audience actually asks, on top of queries tuned to stay fast as data grows.

  • Looker, Tableau, Domo, Power BI, Pentaho
  • Self-serve dashboard design
  • Performance-tuned underlying queries
  • Stakeholder-specific views
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