mitraDATA
Discuss Your Data Challenge

DATA ENGINEERING. BUILT ON TRUST.

Data engineering for
complex operations.

Mitra Data helps organizations connect fragmented systems, build reliable pipelines, modernize data platforms, and create trusted foundations for analytics and automation.

Discuss Your Data ChallengeExplore Our Services
Founder-led engineering. Practical by design.
FROM SOURCE TO CONFIDENCE FIG. 01
APIs
Databases
Applications
Ingest & transformStructured. Repeatable.
ValidateQuality built in
Analytics-ready data
Pipeline healthChecks passing
FreshnessCompletenessConsistency
Observable at every step.Ready for decisions

ILLUSTRATIVE ARCHITECTURE · SYNTHETIC EXAMPLE

THE PROBLEM

When data pipelines fail,
the business feels it.

Late data, changing schemas, and manual fixes turn reporting into firefighting. We make the systems behind your decisions easier to trust, operate, and improve.

Stale or missing data

Decisions can’t wait for yesterday’s data to arrive.

Fragile pipelines

One source change shouldn’t derail your workflow.

Manual reconciliation

Your team has better work than comparing spreadsheets.

Limited visibility

Find the issue before someone questions the report.

WHAT WE DO

Data systems built
to stay reliable.

Explore all services
01

Data Engineering & Integration

Connect fragmented systems with dependable pipelines that move data where it needs to be.

02

Data Reliability & Quality

Make issues visible and keep data fresh, complete, and consistent before it reaches a decision.

03

Cloud Data Platforms

Create a maintainable foundation for analytics and operations, built around your workloads.

04

Data Modernization

Replace fragile scripts and manual exports with workflows your team can maintain.

05

Analytics Engineering

Give reporting teams clean, documented datasets and consistent business definitions.

06

AI & Automation

Reduce repetitive operational work with controlled workflows and the right level of human oversight.

HOW WE WORK

Start small. Prove value.
Scale what works.

A clear first step. A practical path forward.
Engagements built around your actual needs.

01

Discovery

Understand the problem.

Discuss systems, desired outcomes, technical constraints, security requirements, and timing.

02

Define

Start with a bounded scope.

Agree on an assessment or contained project, deliverables, responsibilities, and acceptance criteria.

03

Implement

Build and validate.

Deliver the agreed integration or improvement with testing, documentation, and a clear handover.

04

Support

Keep improving what works.

Successful implementations may lead to scoped monitoring, maintenance, quality checks, and enhancements.

A good place to begin: a discovery conversation, followed by a defined assessment or project.See what’s included

INDUSTRY FOCUS / MANUFACTURING & INDUSTRIAL

Production. Inventory. Quality.
Bring the data together.

Your production data lives in one system. Inventory lives in another. Reporting depends on spreadsheets and manual exports. We can build the pipelines and models connecting those systems.

Explore Manufacturing Data Services
CONNECTED OPERATIONAL DATA
ERPProductionQualityInventory
Connected & validated dataTrusted operational models
Production & ERP dataInventory & qualityQuality & reconciliationFreshness monitoring

Illustrative architecture · Client-controlled environments.

ENGINEERING IN PRACTICE

See how we think
about data reliability.

A reference workflow for manufacturing data.
Real engineering principles. Entirely synthetic data.

Manufacturing data / reliability checksSYNTHETIC EXAMPLE

Synthetic sources: ERP, production system, quality system, and inventory. API and file extracts feed the example below.

  1. 01ERP & operations
  2. 02Ingest & validate
  3. 03Cloud platform
  4. 04Historical data
  5. 05Operational models
  6. 06Reporting & automation
CHECKILLUSTRATIVE RESULTWHY IT MATTERS
FreshnessWithin thresholdReporting uses current data
Duplicate recordsFlagged for reviewProduction events are counted once
Required fieldsValidation passedKey fields are available
Source reconciliationCounts matchMissing records are visible
Schema driftChange detectedUnexpected fields get reviewed
Failed loadsRecovery queuedFailures have a next step

Synthetic demonstration — no client or employer data. Example results illustrate checks, not production performance.

MODERN TOOLS.
PRACTICAL ENGINEERING.

PythonSQLApache SparkDatabricksSnowflakeMicrosoft Azure

WHY MITRA

Focused expertise.
Direct accountability.

A boutique consultancy built around concrete
problems and maintainable solutions.

01

Founder accountability

Sagun leads discovery, architecture, client communication, technical direction, and delivery quality.

02

Reliability first

Build workflows that are supportable, observable, and easier to recover when something changes.

03

Your environment. Your control.

Where practical, work stays inside your cloud account, with clear access boundaries.

04

A bounded place to start

Begin with a defined assessment or project, with clear scope and practical next steps.

SS
Sagun Shrestha

Founder & Data Engineer

Meet the person behind Mitra

LET’S MAKE YOUR DATA DEPENDABLE

Have a data challenge
worth solving?

Let’s start with the problem, the systems involved,
and what a successful first engagement looks like.

Discuss Your Data ChallengeOr, send a message