MV.tech services

Data engineering services for connected, usable business data.

Build the data foundation behind reporting, analytics and business applications. MV.tech designs data pipelines, cloud warehouses and transformation layers that bring information from your systems into a consistent, maintainable platform. We also improve existing pipelines, models and reporting workflows.

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  • Remote from Ahmedabad, India
  • Overlap with your timezone
  • Senior specialists, direct access
  • Scope agreed in writing

The starting point

Bring your business data into one working platform.

  • Connect databases, business applications, APIs and files without relying on recurring manual exports.
  • Give finance, operations and analytics teams shared definitions and datasets.
  • Make data refreshes, failures and reconciliation checks part of a process your team can operate.

What we deliver

From source integration to analytics-ready data.

01

Data integration and pipelines

Collect data from APIs, operational databases and files using Python, SQL and cloud ingestion services. The implementation covers incremental loads, historical backfills, duplicate handling and recovery when a source changes or a run fails.

02

Cloud warehouses and data models

Design BigQuery, Snowflake, Redshift or PostgreSQL environments with separate ingestion, transformation and reporting layers. Define table grain, relationships and business rules so the same dataset can support dashboards, analysis and applications.

03

Data quality and reconciliation

Check source coverage, refresh dates, duplicates and business totals as data moves between layers. Investigate differences between systems and document the rules behind financial and operational metrics.

04

Production delivery and support

Set up scheduling, logs, alerts and deployment workflows. Provide source mappings, model documentation and operating instructions, with ongoing improvements and support available under an agreed scope.

  • Python
  • SQL
  • BigQuery
  • Snowflake
  • Amazon Redshift
  • PostgreSQL
  • layered SQL modelling
  • Azure Data Factory
  • Power BI
  • Tableau
  • Looker Studio
  • Salesforce CRM Analytics
  • AWS
  • Google Cloud
  • Parquet
  • REST APIs

Business applications

Where this service fits.

  • Management reporting. Combine sales, finance and operational sources into a shared warehouse that feeds recurring business reports.
  • A growing data platform. Replace disconnected scripts with managed pipelines, documented models and ownership of each dataset.
  • An existing engineering team. Add specialists for a migration, a new source integration, data quality work or a defined reporting backlog.

Engagement scope

An engagement can cover a new platform or a specific data problem.

We review the sources, consumers and reporting requirements, then agree the architecture and delivery plan. An initial workstream might connect a business application to the warehouse, rebuild an unreliable pipeline or deliver a reporting model. Larger programmes can include staged migration, additional sources and ongoing platform development.

See our delivery process

Working together

How we deliver this work.

  1. 01

    Assess the data estate

    Review sources, existing pipelines, business definitions, access and the reports that depend on them.

  2. 02

    Build the platform and validate it

    Deliver integrations and models in stages, comparing records and business totals with their sources.

  3. 03

    Release and support

    Deploy the schedules and monitoring, document ownership and hand over the operating process.

Teams our engineers have worked with

  • Google
  • Volvo
  • BCW
  • RootstockLabs
  • Chainlabs
  • Toptal
  • Turing
Explore our expertiseMeet Vaghasia on ToptalTimezones, team and engagement costs

Before we begin

Common questions about data engineering consulting

Not answered here? Ask us directly or read the full FAQ.

Can you improve an existing pipeline without rebuilding everything?

Yes. We can start with a bounded review of your existing Python, SQL, cloud and reporting workflows. The scope can focus on unreliable jobs, missing validation, slow queries or a specific integration, preserving the parts that already work.

Can you migrate our reporting to a cloud warehouse without breaking existing dashboards?

That is the normal shape of the work. It starts with discovery: profiling the source databases, documenting source-to-target mappings and running an impact analysis across dashboards and semantic models to find hardcoded rules, dependencies and migration blockers. Layers are then built and reconciled stage by stage against the existing numbers, so reports are cut over on evidence rather than on a date.

Can you work alongside our internal engineering team?

Yes. MV.tech can own an agreed data workstream or provide specialist capacity alongside your team. We agree responsibilities, access, review steps and overlapping working hours before delivery begins.

How is a data engineering project priced?

The estimate depends on source count, access complexity, historical data volume, transformation rules, freshness needs and support. Our remote operating model keeps overhead lean; a useful comparison includes monitoring, documentation and handover as well as the initial build.

What should we share before the first call?

Share the report or workflow you want to improve, the source systems, approximate data volumes, current tools and required refresh frequency. A description or sanitised example is enough for an initial conversation; credentials are not needed in an enquiry.

How do we migrate spreadsheet reporting to a data warehouse without losing trust in the numbers?

Start with one business report that matters and trace its path back to the source systems. We agree which records belong in the result, how fresh they need to be and how correctness will be checked against the numbers people trust today, then build the ingestion, transformations, model and reporting output on BigQuery, Snowflake, Redshift or PostgreSQL. That gives the migration a concrete acceptance test before more spreadsheets, sources or dashboards are added.

Related services

Other ways we can help.

All services

Offshore Data Engineering Team

Extend your delivery capacity with data engineers and analytics specialists from India.

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Data Warehouse Migration

Move reporting and analytics to a cloud platform with a planned transition.

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Business Intelligence & Dashboards

Give business teams a consistent view of performance.

Explore

Related guides

Resources for choosing a data partner.

All insights

Buyer's checklist

12 questions to ask before hiring a remote data consultancy

Who does the work, how the first milestone is scoped and how correctness is proven.

Hiring checklist

Reporting migration

Moving Excel reporting into a cloud data warehouse

Find the source rules, plan the migration order and reconcile the warehouse against existing reports.

Migration guide

Remote delivery

Working with an India-based team across timezones

Realistic overlap windows and the async habits that keep distributed work moving.

Timezone guide

Your next step

Discuss your data platform.

Tell us about the work, your current systems and the people who will use the result. We’ll discuss the requirements, delivery options and what an engagement would include.

Book a 30-minute call Email your brief contact@mvtech.solutions

Remote from Ahmedabad, India · Overlap with any timezone · No obligation