05 · Data Engineering
Data Engineering Services for the AI Era.
Automated ingestion, cloud migration, governed quality, and data products teams can use without a ticket for every repetitive step.
Python, SQL, Spark, dbt, Airflow, Kafka, Snowflake / BigQuery / Databricks
Services for the AI era
What the practice covers
Data Platform Modernization
Automated ingestion and pipelines, migration off legacy and on-prem systems, and multi-cloud and hybrid layouts for real-time analytics and company-wide access.
AI in the Engineering Workflow
MLOps and LLMOps with governance, unstructured sources such as PDFs, decks, and images opened through conversational access, and less hand work in pipeline code.
Platform Operations
Monitoring and incident handling, observability with round-the-clock operations, and FinOps so spend and utilization stay visible.
Data Products
Role-specific products on hubs, meshes, warehouses, and lakes, plus privacy-safe clean rooms when data is shared with partners.
Before and after
Repetitive work moves into the platform
| Before | After |
|---|---|
| Manual ingestion and harmonization. | Flat files become structured data and reporting is automated. |
| Cleansing and standardization by hand. | Quality rules scale across domains. |
| Metadata and catalogs updated by hand. | Catalog enrichment is automated. |
| Errors wait on specialists. | Errors are classified and routed. |
| PDFs, decks, and images stay unused. | Natural-language access to those sources. |
| Insights wait on the data team. | Pipelines recover themselves and people can self-serve. |
Why this practice
What the work is for
Governed Pipelines
Pipelines that stay accurate, consistent, and ready for agents.
AI-Assisted Engineering
Engineers use AI to write, review, and tighten pipeline code.
Scale
Batch and real-time processing for large, distributed workloads.
Lifecycle Governance
Compliance, traceability, and accountability through the lifecycle.
Accelerators
Capabilities that sit on the platform
Quality Guard
Rules, governance standards, and continuous monitoring.
Connect
Multi-source ingest, standard transforms, and scalable pipelines.
Cloud Cost Pulse
Monitoring and recommendations that keep cloud spend in view.
Model Operations
Reusable pieces and automated workflows that put models into production.
Methodology
How a Platform Becomes Data Products People Can Use
Platform and sources, then ingest and harmonize, then quality and metadata, then operate and publish.
- 01
Platform and Sources
Map legacy and on-prem systems, then choose the cloud, hybrid, or multi-cloud layout the pipelines will run on.
- 02
Ingest and Harmonize
Land batch and streaming sources through automated pipelines and standard transforms.
- 03
Quality and Metadata
Apply quality rules, enrich the catalog, and route errors so the same table means the same thing everywhere.
- 04
Operate and Publish
Run the platform with observability and cost visibility, then publish role-specific data products.