Data & ML engineering

From data tointelligencein action.

We build modern data foundations and machine learning solutions that turn your data into reliable, production-ready intelligence.

  • Data strategy
  • Data engineering
  • ML engineering
  • Analytics & BI
  • Data governance
Talk to our teamSee how we build
  • Databases
  • Events
  • Files
  • SaaS APIs
Governed
  • Models
  • Analytics
  • ISO/IEC27001
  • ISO/IEC42001
  • DPA andBAA ready
  • AWS, Azure,Google Cloud
  • All cloudregions

OrbitNexa builds governed data platforms and deploys machine-learning models for healthcare and banking clients on AWS, Azure or Google Cloud: warehouses and lakehouses on Snowflake, Databricks, BigQuery, Redshift or Microsoft Fabric, transformations in dbt with tests on every table, models with a registry and drift monitoring. India regions by default, under ISO/IEC 27001 and ISO/IEC 42001 certified controls.

What we build

Built for the industries we know, on the platforms you already run.

FHIR marts and patient 360 for healthcare. Regulatory reporting with lineage and model governance for banking. And the platform work underneath both.

  • Healthcare & life sciences data

    Clinical and lab data made usable for care, research and reporting.

    HL7 v2 and FHIR ingestion · PHI de-identification pipelines · patient 360 and master patient index · clinical cohorts for research · lab and genomics data marts

    HIPAA · DPDP · ABDM

    FHIRSnowflakedbtAWS

  • Banking & NBFC data

    Reporting a regulator can trace and models a validator can sign.

    Regulatory reporting marts with BCBS 239-style lineage · SR 11-7-aligned model governance · real-time fraud features · credit-risk and collections feature stores · RBI-localised platforms

    BCBS 239 · SR 11-7 · RBI localisation · DPDP

    DatabricksKafkaMLflowAzure

  • Warehouse, lakehouse & migration

    One modelled source of truth, and a named path off what you have.

    Redshift, Teradata or Oracle to Snowflake · Hadoop to Databricks · SSIS, Informatica or stored procedures to dbt · Synapse to Fabric · SQL Server to a cloud warehouse with reconciliation

    Iceberg and Delta · reconciliation before every cutover

    SnowflakeDatabricksBigQueryFabric

  • Pipelines, quality & observability

    Transformations as versioned code, with tests that fail the run rather than the meeting.

    dbt or SQLMesh · Airflow or Dagster · Fivetran, Airbyte, dlt, Debezium · Elementary, Soda, Great Expectations · freshness SLAs with an owner per alert

    Slim CI · state-aware deploys · docs enforced in CI

    dbtAirflowDagsterElementary

  • ML platform & MLOps

    Models with a registry, a rollback and an owner.

    Feature store · MLflow, SageMaker, Vertex or Azure ML registry · CI/CD for models · drift monitoring in Evidently or Arize · SHAP explainability · model cards · LLMOps with prompt and eval versioning

    Approval gate before promotion · SR 11-7 validation

    MLflowSageMakerPyTorchEvidently

  • Governance, semantic layer & BI

    Catalogued, contracted, and one metric definition for every dashboard and model.

    Unity Catalog, Purview, DataHub or OpenMetadata · lineage end to end · data contracts on sources · dbt Semantic Layer, Cube or Metric Views · Power BI, Looker, Metabase · RAG corpus on the modelled layer with an eval harness

    AI-ready data under ISO/IEC 42001

    Unity CatalogCubePower BIGoogle Cloud

Data & ML Engineering

Source to decision.

  • Tested
  • Governed
  • Versioned
  • Semantic layerOne metric, BI and ML.

Our process

From a source register to a model in production.

Discovery week, three two-week sprints with a demo at the end of each, then a close-out your team drives with us on code review.

  1. 01

    Discover

    Discovery & Strategy

    1-2 weeks

    We map your ecosystem, constraints and KPIs before any engineering starts, so every technical decision has a reason on record.

  2. 02

    Model

    Architecture & Design

    2-3 weeks

    Architects draw the system and designers prototype the interface, both reviewed and signed off before a line of code is written.

  3. 03

    Build

    Agile Development

    4-12 weeks

    Iterative sprints on modern frameworks, with a senior engineer reviewing every pull request before it merges.

  4. 04

    Validate

    Quality Assurance

    2-4 weeks

    Automated unit, integration and acceptance tests, plus performance and security checks against real-world scenarios.

  5. 05

    Operate

    Launch & Evolution

    Ongoing

    A zero-downtime deployment, then continuous monitoring and iterative enhancement as your standing technical partner.

The first weeks.

Sources registered, a KPI dictionary, the modelling standard agreed, then three sprints with a demo each.

  1. Week 1Sources registered with an owner and a freshness expectation, KPI definitions written, the dbt project standard agreed: staging, intermediate, marts, naming, test-coverage targets.
  2. Sprint 1The foundation: warehouse or lakehouse, Terraform modules, CI, the first tested pipelines.
  3. Sprint 2Pipelines with tests on every table; freshness SLAs with an owner per alert; the catalog wired.
  4. Sprint 3The modelled layer, the semantic layer and the first dashboards that agree with the source.
  5. Close-outYour team drives the last sprints with us on code review; the standards, CI and runbooks stay in your repository.

Who is on the engagement.

Overlap with UK and Indian working hours every day, a shared channel, a weekly call and a demo at the end of every sprint.

  • Engagement leadA senior data engineer, not an account manager
  • Two to three data engineersModelling, pipelines, tests
  • ML engineerWhen the brief has a model
  • Analytics engineerThe semantic layer and the dashboards
  • Platform engineer, part-timeTerraform, cost governance, catalog
The full process, phase by phase

Let’s build together

Have an idea?
Let’s make it real.

Tell us about your goals. We’ll help you find the right way forward.

  • Share your idea

    Tell us what you're looking to build.

  • Explore possibilities

    We'll understand your goals and suggest the right approach.

  • Plan the next steps

    Together we define the roadmap.

  • Build what's next

    Turn ideas into real impact.

Let’s discuss
your project

Whether it’s a new product, a platform upgrade or a complex challenge, we’re here to help.

Get in touch

Engagement models

Start with the foundation. Models come after the data can carry them.

Shapes described by what happens, how long they run and what you hold at the end. Never an amount.

  • Data Platform Foundation

    A warehouse or lakehouse, the first tested pipelines, and the modelled layer your dashboards will read.

    TeamLead, two data engineers, platform engineer part-time
    Timeline6 – 10 weeks
    Key deliverablesOne governed source of truth and the tests that keep it that way

  • Migration Wave

    Redshift, Teradata, Hadoop, SSIS or Synapse to the target platform, reconciled before every cutover.

    TeamLead, two to three data engineers, your platform owner
    Timeline6 – 16 weeks for the first wave
    Key deliverablesThe first wave live on the new platform, the migration tooling and the runbook for the rest

  • ML to Production

    A model trained on the modelled layer, registered, deployed behind an API or a batch job, and monitored.

    TeamLead, ML engineer, data engineer
    Timeline8 – 16 weeks
    Key deliverablesA model in production with a registry, monitoring and a rollback, then data and ML operations

Start with a data platform and cost review

A 30-minute call, then a written 90-day roadmap

An architecture and warehouse-bill walk-through, a modelling and test-coverage read, a cost-attribution read. You leave with the roadmap and the three changes that matter most. Optional follow-on: a two-to-three-week FinOps or health-check sprint.

Book the review

The standard you keep.

A dbt project standard, CI that enforces it, and runbooks. Contractual deliverables, not internal habits.

  • The dbt project standard

    Staging, intermediate, marts; naming; test-coverage targets. Written down and handed over.

  • Slim CI and state-aware deploys

    Only what changed runs; docs are enforced in CI, not left optional.

  • Terraform modules and the source register

    The platform as code in your repository, with every source's owner and freshness expectation.

  • Knowledge transfer built in

    The last sprints are driven by your team with us on code review.

Done means

A test on every table, an owner on every alert, a card on every model.

What a data platform has to carry before we call it done, and how personal data is handled on the way.

Definition of done

  • Tests on every table: schema, uniqueness, referential integrity, freshness, and the business rules you give us
  • A failing test stops the run and pages an owner; the dashboard keeps the last good data
  • Lineage end to end in the catalog, from source to metric
  • One metric definition in the semantic layer, serving BI and the model alike
  • Every model with a registry entry, an eval against the agreed threshold, an approval before promotion, and a model card
  • Drift and data-quality monitoring with an owner per alert
  • Query attribution and chargeback per team; a monthly cost review

DPDP, UK GDPR and HIPAA by design

  • Region pinning enforced by policy; RBI-localised platforms for payment data
  • De-identification pipelines for PHI and personal data
  • Purpose-limited access through the catalog; audit logs on every query
  • A data-protection impact assessment where the rules require one
  • ISO/IEC 27001 controls on the engagement; AI-ready data under ISO/IEC 42001

AI in our workflow.

We use it, we say so, and nothing it writes merges without a person.

  1. 01We use AI coding tools — GitHub Copilot, Cursor, Claude Code — for scaffolding, tests and migrations, and we say so.
  2. 02Nothing they produce merges without a human review and a passing test suite: the same gate as any other change.
  3. 03The review trail shows who approved what, so an auditor cannot tell a generated line from a typed one, and does not need to.

FAQ

Questions?
We're here to help.

Warehouse or lakehouse, Airflow or Dagster, dbt or SQLMesh: the reasons, written down. Still have a question? We're just a message away.

Get in touch
  1. 01Snowflake, Databricks, Redshift or BigQuery?
    Whichever fits the cloud you already run and the workloads you have. Redshift on AWS, Synapse or Databricks on Azure, BigQuery on Google Cloud, Snowflake or Databricks when you need one warehouse across clouds. The modelling, testing and access-control disciplines are the same on all of them; the recommendation is written into the discovery brief with the reasons.
  2. 02Do you replace our existing dashboards?
    Not unless you want us to. We put a modelled, tested layer underneath them so that what they show agrees with the source, then rebuild only the ones whose numbers were wrong.
  3. 03Can you work with data that cannot leave India?
    Yes. The warehouse, the pipelines and the models run in your own cloud account in an India region, with region pinning enforced by policy and personal data handled under the DPDP Act.
  4. 04How do you stop a bad number reaching a dashboard?
    Tests on every table, run as part of the pipeline: schema, uniqueness, referential integrity, freshness and the business rules you give us. A failing test stops the run and pages an owner. The dashboard keeps showing the last good data rather than the new bad data.
  5. 05Do we need machine learning, or just better data?
    Often the second. The foundation engagement answers that honestly: once the data is modelled and tested, the cases where a model would earn its keep are usually obvious, and so are the cases where a well-built report does the job.
  6. 06Warehouse or lakehouse?
    A warehouse when the data is structured and SQL-first; a lakehouse on Iceberg or Delta when unstructured data, ML and multiple engines need the same tables. Most clients end up with both under one catalog.
  7. 07Airflow or Dagster?
    Airflow when you already run it or need its ecosystem; Dagster for a new platform with a small team, for its asset-based lineage and testing. We write the reasons into the discovery brief.
  8. 08dbt or SQLMesh?
    dbt by default; SQLMesh is worth evaluating above a few hundred models, where virtual environments and column-level lineage pay for the switch.
  9. 09How long does a modernisation take?
    A first wave in six to sixteen weeks depending on the source; the full estate over several waves, with reconciliation before each cutover.
  10. 10How do you handle DPDP, UK GDPR and HIPAA?
    Region pinning, de-identification pipelines, purpose-limited access through the catalog, audit logs on every query, and a data-protection impact assessment where the rules require one.
  11. 11Do you work with Microsoft Fabric?
    Yes: OneLake design, capacity planning, Purview governance and Power BI on the modelled layer, usually for teams moving from Synapse.
  12. 12What does knowledge transfer look like?
    The last sprints are driven by your team with us on code review, and the standards, CI and runbooks stay in your repository.

From the work on this line

Real problems. Real outcomes.

View all insights

Have a data or ML project in mind?

Book a 30-minute discovery call. We'll look at the data you have and tell you what it can support.

Talk to a data engineerEmail hello@orbitnexa.com
+91 912-195-7728Hyderabad, IndiaEvery brief gets a senior review. Reply within 1 business hour, 9 AM-7 PM IST.