Data Platform Engineering
for Enterprise AI-Transformation
From siloed, inconsistent data to a unified, real-time, AI-ready data layer
We turn data chaos like tool sprawl, brittle pipelines, and unreliable dashboards into a governed, cost-controlled data foundation your teams use and your executives trust. We design, build, and run that foundation as a production system in your stack, so AI, analytics, and operations share the same dependable core. We don’t just stand up a lakehouse or wire a few tools together. We re-engineer your enterprise data layer—cloud-native, streaming-capable, and built for AI—with governance, security, FinOps, and AI-augmented operations baked in. The result is faster time-to-insight, predictable spend, and a platform that can keep up with the AI roadmap you’ve committed to.
Client outcomes we’ve delivered with Data Platform Engineering
Annualized EBITDA uplift through a unified data & analytics platform for a North American energy distributor.
Less unplanned downtime by unifying SAP & OT data into a single asset intelligence platform for a leading energy producer.
Improvement in operational efficiency by rebuilding the data model for a North American propane distributor.
The modern enterprise data foundation
Architecture-first, governance-led, engineered for production workloads.
Most enterprises already have a data platform. The problem is what sits behind it: legacy warehouses, half-finished lakes, point solutions, and fragile integrations only a handful of engineers understand. Add batch-heavy ETL, weak DataOps, and limited lineage, and every new use case becomes slow, risky, and expensive.
TechBlocks rebuilds that stack end to end with a vendor-neutral architecture across lakehouse, warehouse, mesh, or fabric patterns, aligned to your regulatory, performance, and AI requirements. DataOps, observability, governance, and AI/agent-assisted operations are built in from day one. Data is treated as a product with clear ownership, SLAs, and quality contracts, not exhaust. The result is an AI-native platform with feature stores, vector stores, real-time serving patterns, and agentic workflows baked in.
Client Success
Reduction in reconciliation
Reduction in unplanned downtime
Faster reporting
Enterprise Asset Intelligence Platform for A Leading North American Clean Power Generation Company
Challenges
Our client is one of North America’s largest and most diverse clean power generation companies, providing baseload and reliable electricity through a mixed fleet of low-carbon assets, including nuclear and hydroelectric power plants. Their analytics and asset management landscape was fragmented across BW on HANA, Power BI, and SQL-based reporting, with heavy manual reconciliation and delayed insights spanning finance, operations, and asset management. The broader SAP estate—SAP ECC, BW, Datasphere, and S/4HANA—added complexity without delivering real-time visibility. The result was siloed data, limited predictive capability, scalability constraints, and governance gaps that slowed decision-making and undermined reliability across 20+ plants.
Solutions
TechBlocks modernized the client’s ecosystem with a unified Business Data Fabric built for real-time asset intelligence and predictive analytics:
- Built a Business Data Fabric anchored in SAP Datasphere, Azure, and Snowflake, centralizing governance and retiring BW on HANA to create a single source of truth and self-serve analytics
- Connected SAP S/4HANA and EAM/PM via Azure Data Factory, and streamed SCADA and AVEVA PI telemetry through Azure IoT Hub and Event Hubs
- Modeled cross SAP and non-SAP data with rich semantics and lineage in DataHub to support consistent, trusted reporting
- Delivered predictive maintenance, OPEX forecasting, and scenario simulation using Azure ML, with SAP Analytics Cloud and Power BI enabling business-led profiling, cleansing, and visualization
Data Platform Engineering Services
Data Strategy, Architecture & Roadmap
We start by getting honest about where you are and where you need to be. We assess your data landscape, governance, and tools, then design a target-state architecture—lakehouse, warehouse, mesh, or fabric—mapped to your use cases, risk, and AI ambitions. We turn that into a funded, sequenced roadmap that prioritizes quick wins, de-risks modernization, and aligns business, data, and IT around the same plan.
Platform Build, Migration & Data Engineering
We use infrastructure-as-code, standardized landing zones, and reusable frameworks to turn designs into cloud-native data platforms, complete with the ingestion and transformation pipelines that feed real workloads. Legacy warehouses, lakes, and Hadoop estates are migrated into a single modern architecture with CDC, streaming, and batch as first-class citizens. The result is a unified, cloud-efficient data foundation that keeps data flowing reliably for analytics and AI.
DataOps, Observability, FinOps & Managed Operations
We bring software engineering discipline and AI-augmented operations to your data stack. Git-based workflows, CI/CD, automated tests, lineage, freshness, and clear SLAs/SLOs become baseline, so changes are safe and repeatable. We layer in cost visibility and guardrails—budgets, right-sizing, auto-scaling—and run the platform with you in an SRE-style model, using AI agents to detect anomalies, pinpoint root causes, and tune for stability and cost control.
Governance, Security & Compliance by Design
We engineer governance into the platform to cut regulatory risk and audit fatigue and to build trust in every dashboard, model, and AI decision. Catalogs, glossaries, classifications, lineage, and policies are built into normal workflows and aligned to domain ownership, so teams move fast without breaking rules. Privacy, retention, residency, and access controls are codified and automated, keeping audits predictable and AI within your risk tolerance.
AI, Analytics & Self-Service Enablement
You turn your data platform into the engine for analytics and AI that show up clearly in your numbers. Semantic layers and metrics stores keep BI, analytics, and data science working from the same definitions. Feature stores, vector stores, and RAG pipelines connect governed data to AI and GenAI workloads, while governed self-service gives business teams access to the data and insights they need without losing control. Copilots and agents plug into the same foundation, using these patterns instead of working around your platform in shadow tools.
Expertise you can trust
Why clients partner with TechBlocks for Data Platform Engineering
We’re engineering-led and AI-native, with vendor-neutral architectures and lean delivery models built for complex, regulated environments. We embed automation, agents, and governance into the platform so control, safety, and explainability are built in—and your analytics and AI are trusted and auditable. By aligning strategy, architecture, and execution, we give you a modern data foundation that ships, scales, and keeps pace with your data and AI roadmap.
“Collaborating with the TechBlocks team on a daily basis has built strong momentum across our client and vendor programs. The most valuable part for us is how they bring clear, tangible results back to our data teams and show how it all connects to the broader enterprise vision.”
CIO – Leading North American provider of utility asset monitoring solutions
Ready to start the conversation?
Share your most pressing challenges and priorities with our team, and let’s explore what’s possible.
