A data engineering FDE pod is 3–5 tkxel engineers who build and fix the pipelines, warehouse models, quality checks and lineage behind one AI use case, inside your cloud and tools, then hand the running platform to your team.
Book discovery callCustomer, product and revenue figures differ between CRM, ERP and spreadsheets. A model grounded on that mix repeats the conflict with confidence.
A schema change upstream silently empties a table. Dashboards go stale and the AI feature answers from data that stopped updating last week.
When an answer looks wrong, nobody can follow it back to the source system and transformation. Audits and model reviews stall on that question.
Report requests and broken jobs fill the week. The foundations an AI use case needs keep slipping to next quarter.
We map the systems your use case depends on, profile their data and agree contracts with source owners on schema, freshness and ownership. Breaking changes then fail loudly at the boundary instead of corrupting tables downstream.
We build batch and streaming ingestion from databases, SaaS APIs, files and event streams using tools such as Fivetran, Airbyte, Kafka or native cloud services. Jobs are orchestrated, retried on failure and versioned in your repository.
We model your data in Snowflake, BigQuery, Databricks, Redshift or Postgres with dbt or an equivalent, from staging layers to clean business entities. Each model has an owner, documentation and tests that run with every build.
We add tests for nulls, duplicates, ranges and referential integrity, plus freshness and volume monitors. Alerts route to the owning team, and failed checks stop bad data before it reaches reports or model inputs.
We capture column-level lineage from source to dashboard and AI feature, with owners, freshness and quality status attached. When an answer looks wrong, your team can trace it back to the exact job and source record.
We prepare the datasets your AI uses: feature tables for models, and cleaned, chunked, permission-tagged documents for retrieval. Refresh schedules keep embeddings in step with source changes, so answers do not drift from current records.
We set role-based access, masking for sensitive fields and retention rules that match your compliance needs. Warehouse usage monitoring and query tuning keep compute spend visible as data volume and AI workloads grow.
Share your use case and sources, and we’ll show you where the gaps are.
Request a data reviewWe profile the systems behind your chosen AI use case and rank data issues by impact. You get a clear view of what must be fixed first.
Deliverables: Source inventory, data profile, issue backlog, target architecture
We stand up ingestion and models for the highest-priority entities in your environment. Quality tests run from the first pipeline onward.
Deliverables: Ingestion jobs, dbt models, quality tests, orchestration setup
We add lineage, monitors, access policies and the AI-ready datasets the use case needs. Your AI or analytics team starts consuming them in staging.
Deliverables: Lineage graph, alerting, access policies, feature and retrieval datasets
Your data team takes ownership of alerts and changes while we pair on live incidents. We finish with documentation and a prioritized roadmap.
Deliverables: Runbooks, catalog entries, ownership map, next-phase roadmap
Scheduled refreshes and freshness monitors keep the inputs to your models and retrieval layers in step with source systems.
Data contracts and build-time tests fail a pipeline before broken records reach dashboards or prompts.
Column-level lineage links each report and AI answer back to the job and system that produced it.
Access policies, masking and documented ownership give reviewers a clear record of who can see what.
Monitoring and runbooks replace manual checking, so fewer hours go to firefighting broken jobs.
Ready to see this in your own workflows?
Book discovery callEach option improves your data; they differ in where the work happens and who keeps it running.
| Criterion | Buy a data platform tool | Traditional data consultancy | tkxel data engineering FDE pod |
|---|---|---|---|
| Starting point | Product features and licenses | Enterprise-wide data strategy | One AI use case |
| Where work happens | Vendor platform configuration | Often a separate project environment | Your cloud and repositories |
| Data quality approach | Built-in monitors you configure | Assessment and recommendations | Tests and contracts in pipelines |
| Lineage and ownership | Automated where integrated | Documented in deliverables | Captured and assigned to owners |
| After delivery | You operate the tool | Roadmap for your team | Running pipelines handed over |
| Fails when | Nobody owns tool configuration | Strategy never reaches pipelines | Source owners reject data contracts |
“Placeholder quote — replace with a real client quote.”
“Placeholder quote — replace with a real client quote.”
“Placeholder quote — replace with a real client quote.”
We build in your repositories and environments from day one, so there is no separate rebuild between proof of concept and release.
Each pod is led by an engineer who has shipped AI systems before, drawn from tkxel’s team of around 150 AI professionals. The same people stay through delivery.
Autonomy matrices, audit trails, and human review are designed with your risk and security teams before release, backed by ISO 27001 controls.
Every engagement starts with agreed business metrics and evaluation sets. We report against them throughout, and the pod exits when your team can run the system.
“Placeholder testimonial — replace with the global set from tkxel.com.”
“Placeholder testimonial — replace with the global set from tkxel.com.”
Data engineering services for AI build the pipelines, storage, quality controls and lineage that give models and AI features accurate, current and permitted data. That includes ingesting from source systems, modeling data in a warehouse or lakehouse, testing it on every run and preparing feature tables or document collections for retrieval. A tkxel data pod does this for one AI use case at a time, inside your own cloud and tools.
AI-ready data is data that is clean, consistent, documented with lineage, refreshed on a known schedule and access-controlled for the way a model or AI feature will use it. A warehouse built for monthly reports is often not enough, because AI features need fresher data, permission tags on documents and records that agree across systems. We define readiness against your specific use case rather than your whole data estate.
We work with the stack you already run, including Snowflake, BigQuery, Databricks, Redshift, Azure Synapse and Postgres for storage, dbt for transformation, Airflow, Dagster or cloud-native schedulers for orchestration, and Fivetran, Airbyte or Kafka for ingestion. For data quality we use dbt tests, Great Expectations or Soda, and we connect to observability tools you already license. We recommend a new tool only when an existing one cannot meet the use case.
We handle data quality with tests and data contracts that run inside the pipelines, and lineage by capturing column-level dependencies from source to output. Tests check nulls, duplicates, ranges, relationships and freshness, and a failed check stops the load rather than passing bad records downstream. Lineage carries owners and quality status, so when a dashboard or AI answer looks wrong your team can find the responsible job quickly.
No, most AI projects can start on your existing warehouse, database or lake if the data for the chosen use case is fixed first. We assess whether your current platform can handle the freshness, volume and access rules the use case needs. A migration makes sense only when the current platform blocks those requirements, and we would scope it as a separate outcome rather than a precondition.
A data engineering pod cannot fix data that is never captured, or business definitions your teams have not agreed on. If sales and finance define revenue differently, we can surface the conflict and model both versions, but someone in your organization has to decide which one the AI should use. Source owners also need to commit to data contracts; without that, pipelines stay fragile no matter how well they are built.
We protect sensitive data by working inside your environment under your access policies, with least-privilege roles, masking and audit logging. tkxel holds ISO 27001 certification for information security management, and our engineers can work with synthetic or masked data in development when production access is restricted. Data does not leave your cloud accounts unless you approve it.