Capabilities Data Engineering & Reporting
Decision-ready information, not more data to manage.
Most businesses have more data than they can use. The problem is not data volume \u2014 it is that the data lives in separate systems, is only accessible to those who built the report, and is always a week out of date. Techseria builds data pipelines, Azure data warehouses, and operational dashboards that give your team live, reliable information without the manual effort.
What Techseria builds for data teams
Azure Data Warehouse
A centralized, structured repository for data from across your business systems ERP, CRM, finance, operations. Built on Azure Synapse Analytics or Azure SQL, with defined schemas and refresh schedules.
Operational Dashboards
Live Power BI or custom dashboards showing the metrics your team uses to manage the business not a static report produced once a month, but a view that updates as your operational systems update.
ETL / ELT Pipelines
Extract, transform, and load pipelines that move data reliably between systems on a schedule or in near-real-time. Built on Azure Data Factory or Python-based pipelines with monitoring and alerting.
Financial Consolidation & Reporting
Automated financial reporting that pulls from ERPNext or your accounting system, applies your consolidation rules, and produces the reports your finance team currently builds manually in Excel.
Inventory & Operations Analytics
Stock level trends, supplier performance, production throughput, and field service efficiency \u2014 visible without opening the ERP and running queries.
When is data engineering the right investment?
A data engineering project adds clear value when:
- Your reporting is done manually in spreadsheets that take significant time to produce each week or month
- Data lives in more than one system and combining it requires manual export and reconciliation
- Decision-makers are working from data that is days or weeks old because live reporting does not exist
- You know what metrics matter but cannot access them without involving the person who built the original report
Technology we use
Azure Data Factory
Managed ETL/ELT orchestration for moving and transforming data at scale. Connects to databases, APIs, file stores, and SaaS platforms.
Azure Synapse Analytics
For analytics workloads that require querying large datasets sales history, production records, multi-year financials at speed.
Power BI
Dashboard and reporting layer connected to your data warehouse. Deployed to your Microsoft 365 tenant so reports are accessible to the right people without additional tooling.
Python & dbt
For data transformation logic dbt provides version-controlled, testable SQL transformations. Python handles API integrations, custom extraction, and automation.
How we deliver data engineering projects
Data Audit & Requirements
We map all data sources, assess data quality, and identify the specific metrics and reports the business needs. The output is a clear list of what will be built and what data quality issues need to be resolved first.
Pipeline & Warehouse Build
We build the data pipelines, warehouse schema, and transformation logic to a fixed-fee scope. Each pipeline is tested against your real data before delivery.
Dashboard Delivery & Handover
Dashboards are built and connected to the warehouse. We train the relevant team members on how to use and maintain the reports, and hand over documentation for the full data layer.
Techseria has built data warehouses and operational dashboards for manufacturers, distributors, and professional services businesses. Projects include ERPNext-to-Azure-Synapse pipelines, multi-site inventory reporting in Power BI, and financial consolidation dashboards replacing monthly Excel processes.
Frequently asked questions
Our data is in ERPNext. Can you build reporting on top of it?
Yes. We build Azure-based data layers on top of ERPNext for clients who need reporting that goes beyond what ERPNext's built-in reports provide particularly for multi-entity, multi-currency, or cross-module analytics.
We use Power BI already but the reports are slow and unreliable. Can you help?
Yes. Slow or unreliable Power BI is usually caused by reports querying source systems directly rather than a properly structured data warehouse. We build the data layer that Power BI should be connecting to.
What if our data quality is poor?
We assess data quality during the audit phase and tell you clearly what needs to be resolved. In some cases we can build data quality checks and transformation rules into the pipeline. In others, the source data needs to be improved before reliable reporting is possible.
Do we need to be on Azure already?
No. We can set up the Azure infrastructure as part of the project. You do not need an existing Azure environment, though we can work within one if it already exists.
Spending too much time on manual reporting?
Tell us what reports your team produces, how long they take, and what data they pull from. We'll tell you what a data engineering project would look like and what it would take to replace the manual process.