Data Engineering Services: Without Clean Data There Is No AI

Snowflake · BigQuery · dbt · Segment · Airflow

Without clean data there's no AI, no personalization, no informed decisions. Data engineering is the invisible layer that makes everything else work: pipelines, warehouses, CDPs and data quality.

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  • 30.7% Sector CAGR (MarketsandMarkets)
How data flows: sources, ingestion, transformation, quality, consumption and an owner per dataset Every incident improves a rule 01 · Sources ERP, CRM, web andfiles 02 · Ingestion Versionedpipelines 03 · Transformation One common datamodel 04 · Quality Rules and alerts 05 · Consumption Dashboards andmodels 06 · Governance An owner perdataset
  1. 01 · Sources ERP, CRM, web and files
  2. 02 · Ingestion Versioned pipelines
  3. 03 · Transformation One common data model
  4. 04 · Quality Rules and alerts
  5. 05 · Consumption Dashboards and models
  6. 06 · Governance An owner per dataset
  7. Every incident improves a rule
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In numbers

The Data and CDP Market

Figures that justify investment in data infrastructure.

  • $8.26B CDP Market 2025 Customer Data Platforms growing at an exponential rate (Grand View Research)
  • $37.1B CDP Projection 2030 30.7% CAGR driven by personalization and AI (MarketsandMarkets, 2025)
  • #1 Top CDP Investment Data integration represents the largest investment in CDP projects
  • -60% Manual Management Reduction AI will reduce manual data management by 60% by 2027 (Gartner, 2025)

What's included

Service Deliverables

End-to-end data infrastructure.

  • Data Pipelines
  • Data Warehouse
  • CDP Implementation
  • Data Quality
  • ETL/ELT
  • ML-Ready Infrastructure

Why

The Modern Data Stack in Action

From raw data to actionable insights.

The modern pattern is ELT (Extract, Load, Transform): you extract data from all your sources (CRM, web, app, ads), load it into a central warehouse, and transform it with dbt (data build tool). Transformations are SQL versioned in Git, testable, documented. No more fragile Python scripts that nobody understands. The result: a warehouse where any team can run reliable queries.

models/marts/customers.sql
-- dbt model: customer lifetime value
WITH orders AS (
SELECT
customer_id,
SUM(amount) as total_revenue,
COUNT(*) as order_count
FROM {{ ref("stg_orders") }}
GROUP BY 1
)
SELECT *, total_revenue / order_count AS aov
FROM orders
  • dbt (SQL) Transform
  • 100% Testable
  • Auto Documented

Definition

What Is Data Engineering?

Data engineering is the discipline that designs, builds and operates the infrastructure to collect, store, transform and serve data at scale. It's the foundational layer that enables analytics, business intelligence, machine learning and personalization.

Without data engineering, analytics teams work with manual CSV exports. ML models train on stale data. An enterprise RAG fed with dirty data gives wrong answers. Data integration represents the largest investment in CDP projects, not the platform. Investing in data engineering first is the most cost-effective decision before buying any analytics or AI tool.

Summary

Executive Summary

For CEOs and data directors.

The CDP (Customer Data Platforms) market will grow from $8.26 billion in 2025 (Grand View Research) to $37.1 billion by 2030 (CAGR 30.7%, MarketsandMarkets, 2025). Data integration represents the largest investment in CDP projects, not the platform itself. Without solid data engineering, a CDP is spend with no return.

Gartner predicts that AI-powered workflows will reduce manual data management by 60% by 2027 (Gartner, 2025). But AI needs clean data to function. Investing in data engineering is investing in the infrastructure that enables all AI, analytics and personalization initiatives.

Kiwop has expertise in Python, web analytics (GA4, BigQuery) and backend development. Data engineering is the infrastructure layer that connects our development, analytics and AI services into a cohesive offering.

  • $37.1B CDP Market 2030 (MarketsandMarkets)
  • #1 Integration = top CDP investment
  • -60% Manual Management (Gartner, 2025)

For the CTO

Summary for CTO / Technical Team

Stack, tools and architecture.

Warehouses: Snowflake (multi-cloud, compute/storage separation, independent scaling), BigQuery (serverless, ideal for Google ecosystem), Databricks (lakehouse, unifies analytics and ML). Selection based on ecosystem, volume, budget and your cloud architecture.

ETL/ELT: dbt for transformations (SQL in Git, tests, auto-generated docs). Fivetran or Airbyte for ingestion (300+ connectors). Airflow or Dagster for orchestration. Everything versioned, reproducible, monitored.

CDPs: Segment (market standard, 400+ integrations), RudderStack (open-source, customer data pipeline), mParticle (enterprise, real-time audiences). Implementation includes identity resolution, consent management, and activation across channels (ads, email, CRM).

Technologies

  • Snowflake
  • BigQuery
  • Databricks
  • dbt
  • Airflow
  • Fivetran
  • Segment
  • RudderStack
  • Python
  • SQL
  • Kafka
  • Docker
  • ELT: Extract (Fivetran) → Load (Warehouse) → Transform (dbt)
  • Orchestration: Airflow/Dagster with DAGs versioned in Git
  • CDP: Ingestion → Identity Resolution → Audiences → Activation
  • Data Quality: dbt tests + Great Expectations + Slack alerts

Who it is for

Is It Right for You?

Data engineering requires data volume and a clear use case. If your company manages data manually, it's time.

Who it's for

  • Companies making decisions based on manual CSV exports and spreadsheets.
  • Analytics teams that need reliable, automatically refreshed data.
  • Organizations planning AI/ML initiatives that need clean data as a foundation.
  • E-commerce and SaaS businesses that want to personalize experiences with unified customer data.
  • Data directors who need a centralized warehouse with governance.

Who it's not for

  • Very early-stage startups with little data and low volume (a CRM is enough).
  • Companies without budget for cloud infrastructure (Snowflake, BigQuery have costs).
  • If you only need a dashboard, no-code tools like Looker Studio may suffice.
  • Organizations with nobody consuming the data (an empty warehouse = spend with no ROI).
  • If your data source is a single app and you don't need to join with other sources.

Key points

Data Engineering Services

Verticals for building your data infrastructure.

  1. 01

    Data Warehouse Design

    Dimensional modeling, staging/marts schemas, partitioning and clustering. Snowflake, BigQuery or Databricks based on your ecosystem. Cost optimization built into the design.

  2. 02

    ETL/ELT Pipelines

    Ingestion with Fivetran or Airbyte (300+ connectors). Transformations with dbt (SQL in Git). Orchestration with Airflow or Dagster. Reproducible, testable pipelines.

  3. 03

    CDP Implementation

    Setup of Segment, RudderStack or mParticle. Identity resolution, GDPR consent management, and audience activation across channels (ads, email, CRM, web).

  4. 04

    Data Quality and Observability

    Automated tests with dbt tests and Great Expectations. Monitoring of freshness, completeness, schema drift. Proactive alerts before users report issues.

  5. 05

    Real-Time Data Streaming

    Real-time data pipelines with Kafka, AWS Kinesis or Google Pub/Sub. For use cases requiring <1 second latency: live personalization, fraud detection, real-time dashboards.

  6. 06

    ML-Ready Infrastructure

    Feature stores, versioned training datasets, data pipelines prepared for machine learning. The foundation for your AI team to work with clean, up-to-date data. The step before running models with LLMOps.

How we work

Implementation Process

From scattered data to centralized infrastructure.

  1. 01

    Data Assessment

    Mapping existing data sources, current quality, business requirements and use cases. Target architecture design with tool selection.

    Week 1–2
  2. 02

    Warehouse Foundation

    Setup of Snowflake/BigQuery/Databricks. Schema design (staging, intermediate, marts). Access policies and governance.

    Week 3–4
  3. 03

    Ingestion Pipelines

    Configuring connectors with Fivetran/Airbyte. First active data pipelines. Integrity validation against the source.

    Week 5–6
  4. 04

    Transformations and Quality

    dbt models for staging and business marts. Automated quality tests. Auto-generated documentation. Orchestration with Airflow.

    Week 7–10
  5. 05

    CDP and Integrations

    CDP implementation (if applicable). Identity resolution and consent management. Audience activation. Connection with analytics and BI tools.

    Week 11–14
  6. 06

    Operations and Continuous Improvement

    Pipeline monitoring, freshness alerts, warehouse cost optimization. Iteration cycles adding new sources and models.

    Ongoing

Risks and how we cover them

Risks and Mitigation

The real risks of implementing data infrastructure.

  1. 01

    Runaway warehouse costs

    Mitigation

    FinOps built in from day 1: clustering, partitioning, auto-suspend, spend alerts. Snowflake and BigQuery charge per query: we optimize every dbt model.

  2. 02

    Poor data quality

    Mitigation

    Automated tests in every pipeline: not_null, unique, referential integrity, freshness. Great Expectations for complex validations. Bad data never reaches marts.

  3. 03

    GDPR non-compliance

    Mitigation

    PII identified and pseudonymized in the ingestion pipeline. Consent management integrated in CDP. Retention policies and right-to-erasure automated.

  4. 04

    Fragile pipelines that break

    Mitigation

    Orchestration with Airflow/Dagster: automatic retries, Slack alerts, circuit breakers. Tests before every deploy. Transformation rollback possible.

  5. 05

    Empty warehouse with no users

    Mitigation

    We start with a concrete use case (dashboard, CDP audience, ML feed), not a generic warehouse. Demonstrable value by week 4.

Technologies

Data Stack

Tools for modern data infrastructure.

  • Snowflake
  • BigQuery
  • Databricks
  • dbt
  • Airflow
  • Dagster
  • Fivetran
  • Airbyte
  • Segment
  • RudderStack
  • mParticle
  • Kafka
  • Python
  • SQL
  • Great Expectations
  • Looker
  • Metabase
  • Docker
  • Terraform
  • AWS Glue

The proof

From Manual CSVs to an Automated Warehouse

Mid-market e-commerce with data scattered across 15 sources: Shopify, GA4, Klaviyo, Meta Ads, Google Ads, ERP, CRM, and more. The analytics team spent 2 days/week preparing data manually. We implemented BigQuery + dbt + Fivetran + Segment: automated ingestion, tested transformations, CDP with activated audiences.

  • 15 Data sources integrated
  • 200+ Automated quality tests
  • <4 weeks Time to first insight
  • Data preparation time -85%

Why

A CDP Without Data Engineering = Wasted Budget

Why infrastructure comes first.

Data integration represents the largest investment in CDP projects, not the platform. Why? Because without clean data pipelines, reliable identity resolution, and tested transformations, a CDP ingests garbage and activates wrong audiences. Investing in data engineering first is the most cost-effective decision before buying any marketing or AI tool.

  • #1 Integration = top CDP spend
  • 3x ROI With Clean Data

FAQ

Frequently Asked Questions About Data Engineering

What data directors and CTOs ask.

What is a data warehouse and why do I need one?

A data warehouse is a centralized database optimized for analytics. It stores data from all your sources (CRM, web, ads, ERP) transformed and ready to query. You need one when your teams waste time preparing data manually or make decisions with outdated data.

Snowflake, BigQuery or Databricks?

Snowflake: multi-cloud, compute/storage separation, ideal for SQL-first teams. BigQuery: serverless, zero management, perfect if you already use Google Cloud and GA4. Databricks: lakehouse that unifies analytics and ML, ideal if you have a data science team. We recommend based on ecosystem and use case.

What is dbt and why is it important?

dbt (data build tool) lets you write data transformations in SQL, version them in Git, test them automatically and document them. It turns the warehouse into a software project with the same engineering practices: CI/CD, code review, tests. It's the de facto standard in the modern data stack.

How much does a data warehouse implementation cost?

Initial setup (warehouse + pipelines + first models): €30K–€60K. With CDP included: €60K–€120K. Monthly infrastructure cost: from €500 (BigQuery serverless) to €5K+ (Snowflake enterprise). Team time savings usually cover the investment in 6–12 months.

Do I need a CDP or is a warehouse enough?

A warehouse is for analytics (querying historical data). A CDP is for activation (sending audiences to channels in real time). If you only need dashboards, a warehouse is enough. If you want personalization, dynamic segmentation or audiences for ads, you need a CDP.

How long does implementation take?

Warehouse + first pipelines: 4–6 weeks. Complete stack with CDP: 10–14 weeks. Demonstrable value (first dashboard with automated data) by week 4. We iterate incrementally; we don't wait for "everything" to be ready.

How do you handle GDPR in data pipelines?

PII (personal data) is identified and pseudonymized in the ingestion pipeline, before reaching the warehouse. Consent management integrated in CDP. Automated retention policies. Right to erasure implemented as a pipeline. Documentation ready for your DPO.

What if my current data quality is poor?

That's where we start. The first phase is a quality assessment: we identify gaps, duplicates, inconsistencies. Then we implement automated tests in every pipeline. Data quality isn't achieved in one shot; it's built with processes and automation.

Can I start small and scale up?

Absolutely. We recommend starting with 3–5 data sources and a concrete use case (a dashboard, a CDP audience, an ML dataset). Demonstrable value in weeks, not months. We scale by incrementally adding sources and models.

Next step

Are Your Data Siloed and Your Teams Wasting Time Preparing Them?

Free data infrastructure assessment. We map your sources, identify quality gaps, and design the target architecture.

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Case studies

Projects where we put it to work

Nexo
AI & SaaS · Our Own Product

Nexo

10+ Live integrations
90% Automated time entries
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Last updated: July 2026

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