Data engineering
Data engineering services for reliable pipelines and AI-ready data
Our data engineering services help operations, IT and data leaders move data from ERP, CRM, plant and SaaS systems into one governed warehouse that reports and AI can trust. We design and build pipelines, models and monitoring for mid-size and large US companies on the platforms you already run. Each project gets a fixed quote after a free review, ships in stages, and runs in your own accounts.
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Quick answer
What are data engineering services?
Data engineering services design, build and run the pipelines that move data from business systems such as ERPs, CRMs and SaaS tools into a warehouse or lakehouse, then clean, test and model it for reporting and AI. The result is one governed, monitored source of data that teams can trust.
- Pipeline design starts with how each source exposes data and how fresh it must be.
- Managed connectors suit common SaaS sources; custom Python suits ERPs and legacy systems.
- ELT is the modern default, but ETL still fits when sensitive data must be filtered first.
- The data stack should be sized to the team that will actually run it.
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What data engineering services include
Data engineering is the work of getting data from where it is created to where it is used, reliably, on time and with clear ownership. Most companies do not lack data. They lack a dependable path from SAP, Salesforce, ServiceNow, spreadsheets and dozens of SaaS tools into a model that finance, operations and leadership read the same way.
We cover the full path from source to dashboard or AI application, and we document it so your team can run it after handover.
- Source assessment, data contracts and target architecture
- Ingestion with managed connectors, APIs, change data capture or file drops
- Warehouse or lakehouse setup on Snowflake, Databricks, BigQuery, Redshift or Microsoft Fabric
- Transformation and testing with dbt, SQL or PySpark
- Orchestration, monitoring, alerting and on-call runbooks
- Serving data to BI tools, operational systems and AI agents
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Data pipeline development
Data pipeline development starts with the source, not the tool. We look at how each system exposes data (API, database replica, change data capture, flat file or event stream), how often the business needs it fresh, and what happens when it breaks. Then we pick the simplest approach that meets those needs.
For common SaaS sources, a managed connector such as Fivetran or Airbyte is usually faster and cheaper to run than custom code. For ERPs, plant historians, legacy databases and APIs without good connectors, we write Python pipelines with retries, logging and idempotent loads. Every pipeline gets freshness and volume checks so a silent failure shows up as an alert, not as a wrong number in a board pack.
WHAT WE BUILD
What we automate
Typical automations, each scoped and quoted at a fixed price after a free review.
ERP to warehouse sync
Orders, invoices and inventory from SAP S/4HANA, Oracle EBS or NetSuite load incrementally into the warehouse several times a day using change data capture or scheduled extracts.
SaaS connector ingestion
Salesforce, HubSpot, ServiceNow and Workday data syncs through Fivetran or Airbyte into raw tables with schema changes tracked.
Spreadsheet and SharePoint intake
Budget files, supplier lists and manual trackers dropped in SharePoint or Google Drive are validated, loaded and rejected with a reason when columns are wrong.
Pipeline health monitoring
Freshness, row count and schema checks run after each load and alert the owning team in Teams or Slack, or open a ServiceNow incident.
Legacy ETL migration
SSIS packages and stored procedures are rebuilt as dbt models and Python jobs, with outputs reconciled against the old system before cutover.
Reverse ETL to operations
Account health scores or inventory positions computed in the warehouse sync back into Salesforce or an ERP so front-line teams see them.
AI-ready document pipeline
Contracts, manuals and reports are extracted, chunked and embedded into a vector index on a schedule so internal AI agents answer from current documents.
03
ETL services vs ELT
Traditional ETL transforms data before loading it into a warehouse, often in a dedicated tool like Informatica or SSIS. Modern ELT loads raw data first, then transforms it inside the warehouse with SQL or dbt, which keeps a full history and makes logic easier to change and review.
Most of our ETL services today are ELT, but not all. Sensitive fields may need masking or filtering before they leave a regulated system, and some high-volume streams are cheaper to aggregate on the way in. We also modernize legacy ETL jobs, moving logic out of SSIS packages or stored procedures into version-controlled, tested pipelines.
04
The modern data stack, and when to keep it simple
The modern data stack is a set of cloud tools that each do one job: ingestion (Fivetran, Airbyte), storage and compute (Snowflake, Databricks, BigQuery), transformation (dbt), orchestration (Airflow, Dagster), BI (Power BI, Tableau, Looker) and reverse ETL back into business systems. It is flexible, but every extra tool adds licenses, integrations and things to monitor.
We size the stack to the team that will run it. A mid-size company may need only managed ingestion, one warehouse, dbt and a BI tool. A large enterprise with streaming plant data and many domains may need Kafka, a lakehouse and a data catalog. We do not add components that nobody on your side can maintain.
05
Working with a data engineering consultant in an enterprise
A data engineering consultant in a larger company works inside existing IT. That means vendor security reviews, SSO and role-based access, least-privilege service accounts, change control for production deployments and audit trails on who changed what. We follow your standards for Git, environments and approvals rather than bringing our own.
For regulated teams (GxP and 21 CFR Part 11 in pharma, GLBA and FFIEC guidance in banking, NERC CIP in utilities), we document data flows, access and lineage to support your validation and compliance work, without claiming compliance on your behalf. As one example from our case studies, a European procurement firm working on nuclear infrastructure saved about 30 hours a month with a supplier intelligence tool built on a structured data pipeline.
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What data engineering services cost
We quote a fixed price after a free review of your sources, platform and reporting goals. The main cost drivers are the number and difficulty of sources, data volume and freshness needs, how much legacy logic must be migrated, and security or validation requirements.
A first production pipeline into an existing warehouse typically ships in 2-4 weeks. A new warehouse with several sources and tested marts usually takes 4-8 weeks in stages. Cloud platform and connector fees are billed to your accounts directly, and we help you estimate them up front.
TOOLS
Tools we connect
FAQ
Data Engineering Services questions
What do data engineering services include?
Data engineering services cover moving data from source systems into a warehouse or lakehouse, transforming and testing it, orchestrating and monitoring the pipelines, and serving the results to BI tools, operational systems and AI applications.
What is the difference between ETL and ELT?
ETL transforms data before loading it into the warehouse. ELT loads raw data first and transforms it inside the warehouse, usually with SQL or dbt. ELT is more common on modern cloud platforms, though ETL still fits where sensitive data must be filtered first.
Should we build custom pipelines or use Fivetran or Airbyte?
Use managed connectors for common SaaS sources where they work well, because they cost less to maintain. Build custom Python pipelines for ERPs, legacy databases, plant systems and APIs that connectors do not cover or cannot handle reliably.
When should we hire a data engineering consultant?
Typical triggers are reports that disagree, analysts spending hours each week on manual exports, a new ERP or warehouse project, or AI plans that stall because the data is not ready. A consultant can also supplement an in-house team during a migration.
Which data warehouse should we choose?
It depends on your cloud, skills and workloads. Snowflake suits SQL-first analytics, Databricks suits Spark and machine learning heavy teams, BigQuery suits Google Cloud shops, and Fabric suits Microsoft-centric companies. We recommend after reviewing your stack.
Who owns the pipelines after the project?
You do. Code lives in your Git repository and runs in your cloud accounts. We document each pipeline and run a handover so your team or another provider can maintain it.
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Describe the process that eats your team's week. We reply within one business day with a first take on what can be automated, which tools fit and roughly what it would cost.
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- Fixed quote before any work starts
- You own every workflow, account and line of code