Data engineering services that make every number trustworthy
We design and run the pipelines, lakehouses and semantic layers that give your dashboards, finance team and AI models the same answer to the same question. Our data engineering services cover architecture, ingestion from ERP, CRM and devices, tested transformations, governance and analytics, built on Databricks, Delta Lake, dbt and the Azure, AWS or Google Cloud estate you already run.
Why reports disagree and AI projects stall on data
When finance and operations present different revenue figures in the same meeting, the problem is rarely the dashboard tool. It is three definitions of "customer", a pipeline that silently skipped yesterday's load and a spreadsheet step nobody documented. The same gaps stop AI projects.
We fix the plumbing before the presentation layer. Sources are inventoried, metrics are defined once with a business owner, pipelines are tested like software and failures page someone. A report, a forecast and an agent then draw from the same governed tables.
What does a data engineering consulting company deliver?
Data engineering is the work of moving data from where it is created to where it is used, correctly, on time and with a record of what happened to it on the way. A data engineering consulting company earns its fee by making that boring, so analytics can be interesting.
In practice we build lakehouses on Databricks and Delta Lake with bronze, silver and gold layers, model transformations in dbt or Spark, orchestrate them on tested schedules and expose results through a semantic layer to Power BI, Tableau or your applications. We have delivered this pattern for industrial IP intelligence and multi-site telemetry, and the same tables feed our AI and data science work.
Our data engineering and analytics services
Six services, from the architecture decision to the dashboard. Most clients start with architecture and one high-value pipeline.
Enterprise data architecture
Target state for data models, integration patterns, storage tiers and governance. We decide warehouse, lake or lakehouse on your workloads and cost, and write down why, so the next team understands the choice.
ETL, ELT and integration
Tested pipelines from Dynamics 365, SAP, Oracle, Salesforce, databases, APIs and files, built with Azure Data Factory, AWS Glue, Google Dataflow or Databricks workflows, with incremental loads, schema change handling and replay.
Cloud data platforms and lakehouse
Databricks and Delta Lake lakehouses, Snowflake or BigQuery warehouses, and Microsoft Fabric where it fits, with Unity Catalog or equivalent governance, medallion layers and compute cost controls from day one.
Streaming and real-time analytics
Kafka, Event Hubs, Pub/Sub and MQTT streams processed with Spark Structured Streaming or Flink into time-series stores and live dashboards, with delivery guarantees and latency targets per feed.
Data quality and observability
Tests on every transformation, freshness and volume checks, column-level lineage, access logging and an on-call runbook, so a broken feed is caught before the board pack is built.
Business intelligence and analytics
A semantic layer with one definition per metric, Power BI or Tableau models on it, self-service datasets for analysts and the few executive dashboards that actually get opened.
Enterprise data use cases we have built or scoped
Patterns from our delivery and product work, without client names or figures.
Unified sales and financial analytics
Orders, invoices, payments and ledger entries from ERP and billing systems conformed into one gold layer, so revenue, receivables and margin reconcile between finance and sales dashboards.
Operational KPI dashboards
Plant, store or network KPIs refreshed hourly through a semantic layer, with freshness shown on the dashboard so a manager knows whether the number is from this morning or last week.
ERP and CRM data harmonization
Customer, product and supplier master data matched across Dynamics 365 Business Central, CRM and legacy systems, with survivorship rules agreed by data owners and a stewardship queue for conflicts. This underpins our AP automation product.
IoT telemetry pipelines
Sensor, server and network telemetry from multi-site GCC estates ingested over MQTT and Telegraf into TimescaleDB, downsampled for long-term storage and exposed to anomaly models and Grafana. See energy and utilities.
AI-ready document and text stores
Contracts, patents and research documents parsed, deduplicated, chunked and indexed in a governed lakehouse with lineage to the source file, feeding assistants under enterprise knowledge intelligence.
Legacy database consolidation
On-premises SQL Server and Oracle databases migrated into a cloud lakehouse with change capture, parallel-run reconciliation and a cut-over plan, decommissioning servers only after every report matches.
How we deliver a data platform in seven steps
Seven steps from source inventory to analytics delivery. Metric definitions come second, before architecture, because the hardest arguments are about what "active customer" means, not which cloud to use.
- 01
Source inventory
Every system, table, owner, refresh cadence and sensitivity.
- 02
Business metric definitions
Each metric defined once, with an owner and a worked example.
- 03
Architecture
Lakehouse or warehouse, cloud, tooling and cost model, with reasons.
- 04
Ingestion
Connectors and change capture landing raw data in the bronze layer.
- 05
Transformation and tests
dbt or Spark models with tests, lineage and code review.
- 06
Governance
Catalog, permissions, retention and residency controls, audited.
- 07
Analytics delivery
Semantic layer, dashboards and self-service datasets handed to your team.
Where we deliver: from Chennai, Bangalore, Abu Dhabi, Saudi Arabia and Branchburg, New Jersey. In India we design to the DPDP Act 2023 and RBI localization expectations. In UAE, UAE PDPL and the e-invoicing rollout shape finance pipelines. In Saudi Arabia, SDAIA's PDPL and NCA ECC-2:2024 require in-Kingdom hosting for many workloads, and ZATCA e-invoicing drives finance integration. In United States we align to NIST privacy and security frameworks. See our locations.
Business outcomes and how we measure a data platform
A data platform is working when people stop exporting to Excel to check the numbers. We measure freshness against agreed targets, reconciliation between source and gold tables, pipeline success rate and time to recover, query performance for the top dashboards, access compliance from audit logs and total monthly cost including compute.
The business outcome shows up as time: days saved closing the month, hours analysts stop spending on cleanup, how quickly a new question gets a trusted answer.
Security, governance and quality for enterprise data
Every platform applies least-privilege access with row and column permissions, catalogs each dataset with its owner and classification, logs every access and keeps data in the region your regulator requires. Personal data is masked or tokenized in the silver layer so analysts and models work on protected columns by default.
Changes go through code review and automated tests before they touch production tables, and test evidence is kept per release. Regulated workflows in banking, healthcare and government add legal, security and compliance review. Our practices are ISO 27001-aligned.
Why Constient for data engineering
We build data platforms because our AI, edge and application work depends on them.
Built for the models that follow
Our data scientists consume the tables we build, so training and production data are the same governed data, and feature pipelines are designed in, not bolted on.
Technology we work with daily
Databricks, Delta Lake, dbt, Spark, Kafka, TimescaleDB, Power BI and the native data services of Azure, AWS and Google Cloud. We choose on your workloads.
Edge and telemetry experience
Our edge AI team brings device data in over MQTT and SNMP, so time-series and streaming are first-class, not an afterthought to batch.
Platform engineering included
Landing zones, CI/CD and observability come from our cloud and DevOps practice, so data pipelines ship with the same discipline as application code.
Regulated-market delivery
Work for securities firms in India and operators in the Gulf has taught us what residency, access evidence and audit reviewers ask for.
Honest about cost
Lakehouse compute bills surprise people. We set budgets and alerts from day one and will say when a simpler warehouse beats a lakehouse.
Frequently asked questions
How does a data engineering engagement with Constient start?
With a source inventory and metric workshop over 3 to 4 weeks. We list your systems, owners and refresh cadences, define the top 20 to 30 metrics with the people who argue about them, and return a target architecture with a cost model and a first pipeline to build. You can stop there with a roadmap or continue.
Do we need a data lake, a warehouse or a lakehouse?
It depends on your data formats, consumers and cost. Structured finance and sales data with BI consumers often suits a warehouse. Video, documents, telemetry and machine learning favor a lakehouse on Databricks and Delta Lake. Many clients end up with a lakehouse and a warehouse-style gold layer. We decide on your workloads and write down the reasons.
Can the platform work with our legacy databases and ERP?
Yes. We connect to on-premises SQL Server, Oracle, Dynamics AX and Business Central, SAP and custom applications through change data capture, scheduled extracts or APIs, with incremental loads and quality checks on arrival. Legacy systems stay for a parallel run until reports reconcile.
Where will our data be stored and processed?
In the region your regulator requires, on your own Azure, AWS or Google Cloud tenancy or your data center. For Saudi clients we design for in-Kingdom hosting under NCA and SDAIA requirements, for UAE clients under UAE PDPL, and for Indian clients to the DPDP Act 2023 and RBI expectations. Residency is decided at the architecture step.
How does data engineering support our AI plans?
By giving models the same governed tables that reports use, with lineage to source, masked personal data and feature pipelines that run in production as well as training. Without this layer, AI projects spend most of their budget on cleanup.
How is data engineering work priced?
The inventory and architecture phase is fixed price. Platform build is priced per phase against agreed acceptance criteria, usually one data domain at a time. Ongoing operation runs as a dedicated data engineering team or a support retainer. We give you a compute and storage estimate alongside the build cost.
What do you need from us to begin?
A sponsor who owns the metric definitions, read access to source systems or extracts, a security contact for residency and permission decisions, and a few hours from the analysts who build today's reports. With those we can produce the inventory, definitions and architecture inside 4 weeks. Contact us to start.
Build your data engineering and analytics roadmap
Tell us which numbers your teams disagree about, which systems they come from and what you want to build on top. We will return an architecture view, a cost model and the first pipeline worth building.