Snowflake or Databricks—which platform is right for us?

We decide this together using an evaluation grid rather than based on a preferred platform: dominant use cases, existing team skills, current cloud setup, data volume, latency, compliance, and cost patterns. We’re certified for both and aren’t tied to any commission—often, a combination of the two or Microsoft Fabric is the better solution.

How much does a data engineering project cost—and at what point does it pay off?

We’ll kick off with a business case in about two weeks. The cost of delay and unoptimized cloud costs are usually more expensive than the project itself; built-in FinOps typically reduces cloud costs by 20–40%. Depending on the project, billing is either a fixed-price package or a time-and-materials model.

Can't we set this up ourselves in-house?

You can—though doing it in-house usually takes twice as long. We provide proven templates and access to vendors, and hand over the platform fully configured so your team can continue managing it on its own.

How do you ensure data privacy and compliance in data pipelines?

We incorporate compliance as a gate in the pipeline, not as a downstream audit. This includes quality testing with dbt, Great Expectations, and Soda; a data catalog with automated lineage; access control models using RBAC and ABAC; tag-based masking and PII classification; as well as GDPR-compliant pseudonymization and data erasure strategies. We factor in the Data Act, NIS2, and the EU AI Act from the very beginning.

How does data engineering fit into our AI strategy?

AI is only as good as the data it’s based on. For GenAI today, even unstructured data—such as documents and texts—must be usable, classified, and tagged with lineage. That’s why we’re designing the platform from the ground up to handle both structured and unstructured data, ensuring the data readiness that models can rely on.

A person wearing a hard hat is kneeling in front of a tall tower made of wooden blocks and placing a building block at the base, bathed in blue and red light

Data Engineering Services

Your Foundation for Reliable Data, Analytics, and AI

Before AI can deliver results, the data has to be right

AI and analytics projects stand or fall on the quality of the data. When numbers contradict each other, platforms remain half-empty, and pipelines run without testing, even the best model won’t help.

We lay the foundation: integrated, quality-assured data that’s reliably available —operated in production rather than as a proof of concept gathering dust in a drawer.

Less Technology. Better Business.

Schedule a free consultation

Full-scale implementation instead of a pilot project

About 70% of our projects result in a fully operational platform—with tested code and senior engineers on board from day one.

Architecture Without the Clutter

Our Medallion Reference Architecture supports the use case with 20–30% fewer components—it’s lean, maintainable, and free of over-engineering.

Manufacturer depth without commitment

Certified on Snowflake, Databricks, Microsoft, and AWS, but with no commission—we recommend what your use case needs, never what’s best for our bottom line.

Costs from the very beginning

We incorporate FinOps from day one: tagging, right-sizing, and alerts typically reduce your cloud costs by 20–40% compared to standard practices.

Data Engineering Services: The Foundation of Analytics and AI

Data engineering services encompass the expertise and infrastructure that ensure your data is reliably available, integrated, quality-assured, and usable —including platforms, pipelines, modeling, and operations. They form the foundation upon which analytics and AI can be built in the first place, and are therefore almost always the critical path. As a data engineering consulting firm, we combine strategy, architecture, and tool selection.

Within Data Engineering Services, we focus on the data foundation—the bedrock. Our sister teams in Data Science, AI, Analytics, and Governance Consulting handle the models built on top of it, BI reporting, and comprehensive governance programs, so you get everything from a single source. There’s only one thing we deliberately avoid: pure tool licensing and reselling—this ensures our recommendations remain unbiased.

DWH Consulting, DWH Services, and Data Integration—three specialized services

Three terms that are often used interchangeably actually have different meanings:

Data Warehouse Consulting: the conceptual side—modeling and architecture.
Data Warehouse Services: the implementation side—setup and operation.
Data Integration Services: the specific task of connecting source systems and consolidating data.

Whether ETL or ELT, data lake or lakehouse, data mesh or streaming—we classify these terms as needed. ELT dominates in modern cloud and lakehouse architectures, while ETL remains relevant in regulated or legacy scenarios.

Strategy & Assessment: Maturity Level, Target State, Prioritized Roadmap with Business Case.
Architecture & Design: Target architecture based on Lakehouse, data warehouse, or streaming; data model and security concept.
Platform Architecture: Cloud-native, with Infrastructure as Code, CI/CD, a data catalog, and observability.
Pipeline Development & Integration: heterogeneous sources, batch and streaming, with built-in quality checks.
Migration & Modernization: Moving a Legacy Data Warehouse to a Cloud Lakehouse—Low-Risk and in Phases.
Operations & Optimization: Run, SRE Mindset, FinOps, and Performance Tuning.

When Building a New Home Is Worth It—and When It Isn't

Most projects start from one of four scenarios. AI or GenAI is planned, but the data isn’t ready—it lacks quality, integration, or a governed platform. A legacy data warehouse like Oracle, Teradata, or SAS is expensive, slow, and at the end of its lifecycle.

Data silos produce conflicting figures, and reporting loses credibility. Or a cloud migration is hampered by growing data volumes and increasing regulatory requirements—DORA, NIS2, and the EU AI Act are ramping up the pressure for traceability.

From the CDO to Executive Management

The decision to adopt a data platform usually involves several stakeholders—each with different priorities. We address each one with the argument that matters most to them:

CDO / Head of Data

Faster, more reliable data for analytics and AI, as measured by time-to-insight.

IT Management/CIO

Stability and manageable costs through an SRE mindset, automation, and FinOps.

Department

Finally, reliable data and self-service—no more waiting on hold with IT.

Management

Investment security through a clear business case, ROI, and cost of delay.

Energy Industry & Utilities

Smart meter data, market communication, and time series processing on KRITIS-compliant platforms—with the rollout under the MsbG, these will become massive time series that place high demands on performance and governance.

Typical use cases: feed-in and load forecasts for solar and wind, grid utilization analyses, and asset monitoring. The higher the share of volatile generation, the more forecast accuracy and grid stability depend on reliable real-time data.

Manufacturing & Industry

MES, shop floor, and sensor data; OT/IT integration via OPC UA and MQTT; plus streaming and edge connectivity.

This gives rise to specific use cases: predictive maintenance, OEE and scrap analyses, track & trace, and the digital twin of a plant. The true added value comes from combining years of machine history with real-time data streams—the foundation for reliable predictions rather than isolated dashboards.

Insurance

Claims and contract data models, actuarial data flows, and BCBS 239- and DORA-relevant data lineage—with IFRS 17 and Solvency II, the demands on data volume and quality are increasing even further.

This gives rise to use cases such as fraud detection in claims, underwriting and pricing analytics, and reliable reserve data. Because this data has historically been stored in separate systems, its clean integration is a prerequisite for each of these analyses.

Automotive & Mobility

Telematics and connected vehicle data with high volume and streaming requirements, increasingly regulated by the EU Data Act and the GDPR. These data are used for fleet management, the analysis of driving and ADAS data in development, and for Mobility-as-a-Service. As the fleet grows, the architecture determines whether telemetry data can continue to be analyzed in real time or whether it becomes overwhelmed by the sheer volume of data.

Phase 1: Diagnosis & Target State

In one to two weeks, we’ll talk with your stakeholders, assess your resources, current state, and maturity level, and refine your vision. The result will be a prioritized roadmap with a business case and initial quick wins—a clear roadmap instead of a mountain of concepts.

Phase 2: Architecture & First Breakthrough

During the architecture design phase (three to four weeks), we define the target architecture, tool selection, data model, and security and FinOps concepts. The PoC then runs an end-to-end process—including tests and a catalog—with the first run typically completed in four to six weeks.

Phase 3: Development & Integration

Over a period of three to nine months, we scale up to include additional sources and domains and migrate legacy systems in phases. Depending on the project, we work on a fixed-price basis, on a time-and-materials basis, with a dedicated team, or—most commonly—with a hybrid team, supplemented by south-shoring with senior management oversight.

Phase 4: Operation & Optimization

After go-live and hypercare, we can take over day-to-day operations upon request: Run, SRE mindset, FinOps, and tuning—either as a managed service or as part of the customer’s team. We are guided by hard KPIs—time-to-insight, pipeline reliability, data quality, cloud costs, and ROI.

Frequently Asked Questions About Data Engineering Services

Snowflake or Databricks—which platform is right for us?

We decide this together using an evaluation grid rather than based on a preferred platform: dominant use cases, existing team skills, current cloud setup, data volume, latency, compliance, and cost patterns. We’re certified for both and aren’t tied to any commission—often, a combination of the two or Microsoft Fabric is the better solution.
How much does a data engineering project cost—and at what point does it pay off?

We’ll kick off with a business case in about two weeks. The cost of delay and unoptimized cloud costs are usually more expensive than the project itself; built-in FinOps typically reduces cloud costs by 20–40%. Depending on the project, billing is either a fixed-price package or a time-and-materials model.
Can't we set this up ourselves in-house?

You can—though doing it in-house usually takes twice as long. We provide proven templates and access to vendors, and hand over the platform fully configured so your team can continue managing it on its own.
How do you ensure data privacy and compliance in data pipelines?

We incorporate compliance as a gate in the pipeline, not as a downstream audit. This includes quality testing with dbt, Great Expectations, and Soda; a data catalog with automated lineage; access control models using RBAC and ABAC; tag-based masking and PII classification; as well as GDPR-compliant pseudonymization and data erasure strategies. We factor in the Data Act, NIS2, and the EU AI Act from the very beginning.
How does data engineering fit into our AI strategy?

AI is only as good as the data it’s based on. For GenAI today, even unstructured data—such as documents and texts—must be usable, classified, and tagged with lineage. That’s why we’re designing the platform from the ground up to handle both structured and unstructured data, ensuring the data readiness that models can rely on.

Data Engineering Services

Before AI can deliver results, the data has to be right

Engineering That Makes a Difference in Production

Full-scale implementation instead of a pilot project

Architecture Without the Clutter

Manufacturer depth without commitment

Costs from the very beginning

Data Engineering Services: The Foundation of Analytics and AI

Data Engineering, Analytics, or Data Science—Which Comes First?

DWH Consulting, DWH Services, and Data Integration—three specialized services

Here's How We Build Your Data Platform

Six interlocking building blocks

DWH Modernization: Replacing Legacy Systems Without a "Big Bang"

Data Integration: Reliably Connecting Heterogeneous Sources