Data engineering consulting connects your source systems, builds the pipelines that move and clean the data, and hands over a warehouse or lakehouse that analytics and AI can trust. In India, indicative engagements run from about ₹3 lakh for one or two sources to over ₹1 crore for a multi-unit migration — and running it is a separate budget.
Both halves matter. Gartner predicts that through 2026 organisations will abandon 60% of AI projects that are not supported by AI-ready data, and found that 63% of organisations either lack, or are unsure whether they have, the right data management practices for AI. And building the pipelines is only the start: Fivetran’s 2026 benchmark of large enterprises found that 53% of data engineering capacity goes to maintaining and troubleshooting pipelines that already exist.
This guide covers what is in scope, what the systems you probably run make difficult, what it costs to build and to keep running, and the questions that tell you whether a provider will still be answering the phone in year two.
What data engineering consulting covers
A complete engagement has six workstreams. Proposals that price only the first three are pricing a build, not a data platform.
- Source integration. Getting data out of the systems that hold it ERP, CRM, core banking, Microsoft 365, accounting, and the in-house application nobody has documented reliably, incrementally and within each system’s rules.
- Pipelines and orchestration. Scheduling, dependencies, retries and backfills, so that a failure at 2 a.m. is retried or reported rather than silently producing yesterday’s numbers.
- Warehouse or lakehouse. Where the data lands and is modelled into tables people can use: a cloud warehouse such as Snowflake or BigQuery, a lakehouse such as Databricks, or — more often than vendors admit a well-run Postgres.
- Data quality and observability. Tests on freshness, volume, uniqueness and business rules, run inside the pipeline, with alerts that reach a named person before they reach the finance team.
- Governance and access. Who can see what, how personal data is masked, where it is stored, and how long it is kept the parts the DPDP Act will ask about.
- Handover and run. Runbooks, documentation, an on-call rota, and a written answer to the question of who owns each pipeline after the consultants leave.
The first three are engineering. The last three decide whether anyone trusts the numbers. In Monte Carlo’s 2023 survey of data leaders, 74% said business stakeholders are the first to find data issues, ahead of the data team — which is what happens when quality and ownership are left out of scope.
The systems nobody wants to touch
Every Indian enterprise has at least one. The table sets out how each is usually connected and where the effort goes.
Common source systems and what connecting them involves
| Source system | Usual connection | What goes wrong | Indicative effort for one production connector |
|---|---|---|---|
| SAP ECC or S/4HANA | Released OData or SOAP APIs, SAP Integration Suite, or a certified extraction or change-data-capture tool | Custom Z-tables with no API, authorisation roles, and an ERP migration happening mid-project | 3–6 weeks per functional area |
| Salesforce | REST and Bulk APIs, or a managed connector | API call limits, custom objects, and field history nobody asked for until it was missing | 1–2 weeks |
| Microsoft 365 and SharePoint | Microsoft Graph with site-scoped permissions | Permission sprawl, and content that is documents rather than tables | 2–4 weeks |
| TallyPrime | XML over HTTP, native JSON from TallyPrime 7.0, or ODBC | Multiple company files, desktop installations and version mismatches across branches | 1–3 weeks |
| Core banking, loan origination or loan management systems | Vendor APIs, database replicas or nightly files | Batch windows, vendor change requests and contractual limits on direct access | 4–8 weeks |
| An in-house application with no API | A read replica with change-data capture, or scheduled extracts | An undocumented schema and one person who understands it | 3–8 weeks |
SAP deserves a specific warning. SAP provides mainstream maintenance for Business Suite 7 until the end of 2027, with extended maintenance to the end of 2030 at a premium of two percentage points. If your SAP estate is moving in the next three years, build the pipelines against SAP’s released APIs, which SAP says stay stable from release to release, rather than against ECC tables that will not exist after the migration. Pipelines built on table reads are the ones that get rebuilt.
Tally is the Indian special case. It is the book of record for a large share of mid-sized businesses and for many branches of large ones, it usually runs on a desktop, and there is often one company file per entity. The integration itself is simple TallyPrime exposes its data as XML over HTTP and, from version 7.0, as JSON. The work is in finding every installation and agreeing when each one is switched on. Build these connectors once and properly: they are the same ones you will need when connecting AI to these systems later.
What data engineering consulting costs in India
Indian data engineering firms bill in a well-established band. Clutch’s data and analytics pricing guide, updated in September 2026, puts the average at $25–49 an hour, with India inside that band, and says projects reviewed on the platform typically cost $10,000–49,999. GoodFirms reports a median of $37 an hour for big data analytics firms in India.
Applied to realistic team shapes, that gives three bands:
Indicative cost by scope, at $25–49 an hour
| Scope | Typical team | Duration | Effort | Indicative cost |
|---|---|---|---|---|
| One or two sources into an existing warehouse, scheduled refresh, basic tests | One data engineer and a part-time architect | 3–6 weeks | 150–300 hours | $3,750–14,700 (about ₹3.3–12.9 lakh) |
| Five to ten sources, a new cloud warehouse, modelled tables, data-quality checks, reporting-ready outputs | Two or three data engineers and an architect | 8–12 weeks | 1,000–1,600 hours | $25,000–78,400 (about ₹22–69 lakh) |
| Legacy ERP extraction and a lakehouse across business units, with change-data capture, governance and an access model | Four to six data engineers, an architect and a tester | 4–6 months | 3,000–5,000 hours | $75,000–245,000 (about ₹66 lakh–₹2.2 crore) |
Rupee figures at ₹88 to the US dollar. Effort ranges are indicative; cost is effort multiplied by Clutch’s published India band.
What moves a project between bands is rarely the headline tool. It is the number of sources and how clean they are, how many years of history must be backfilled, how fresh the data has to be (daily is cheap; streaming is not), whether personal data needs masking and residency controls, and how many teams will consume the output.
It is also worth knowing the in-house comparison. Glassdoor India puts the average data engineer at ₹11 lakh a year, a senior data engineer at about ₹20.7 lakh and a data architect at about ₹28.3 lakh. A consultancy makes sense for the build and the design decisions; the run is often better owned by one or two people of your own, trained during the project.
What it costs to keep running
This is the section most proposals leave out, and the one your finance team will ask about in month four. The Fivetran benchmark cited above, a survey of 500 senior data leaders at companies with more than 5,000 employees, found an average of 4.7 pipeline failures a month, more than 60 hours of monthly downtime, and close to 13 hours to resolve each incident. Its companion report puts engineering labour spent on pipeline maintenance at $2.2 million a year for those companies.
The run-rate, line by line
| Cost line | What drives it | How to keep it down |
|---|---|---|
| Warehouse compute | Query volume and warehouse size. Snowflake lists on-demand credits at $2, $3 and $4 by edition, the same in its AWS Mumbai region; BigQuery charges $6.25 per TiB scanned on demand, with the first TiB each month free | Auto-suspend, partitioning and clustering, and capacity pricing once usage is predictable |
| Storage | Data volume and how many copies you keep. Snowflake on-demand storage lists at $23 per TB a month | Lifecycle rules, and dropping staging copies nobody reads |
| Ingestion tooling | Managed connectors priced on usage — Fivetran charges on monthly active rows, with a free plan up to 500,000 | Sync only the tables and columns you use, at the frequency you need |
| Monitoring and on-call | Number of incidents multiplied by time to resolve | Tests inside the pipeline, alerts routed to a named owner, and runbooks for the common failures |
| Upstream change | Vendor upgrades, new fields, renamed columns | Agreements with source-system owners and automatic schema-change alerts |
| Engineering time | Everything above, plus new requests | Budget it explicitly — at 53% of capacity, maintenance is the job, not an overhead |
Ask every provider to put a run-rate in the proposal, split into cloud costs and people. A provider who cannot estimate the cost of running what they build has not run it.
The data-quality tests that matter
Data quality sounds like a discipline; in practice it is a short list of tests that run every time a pipeline does. Seven cover most of what goes wrong.
Seven tests every production pipeline should run
| Test | What it catches | Example |
|---|---|---|
| Freshness | Data that stopped arriving | The sales table has not updated since 2 a.m. because a source credential expired |
| Volume | Partial loads and duplicated loads | Yesterday’s invoices are 40% below the weekday average, or exactly double |
| Schema | Upstream changes | A column was renamed in the ERP and now arrives empty |
| Uniqueness | Duplicate records | The same loan account appears twice after a retry |
| Referential integrity | Orphaned records | Transactions that point to customers who do not exist in the customer table |
| Accepted values and business rules | Values that are valid data but wrong business | A GST rate outside the permitted slabs, or a disbursal date before the sanction date |
| Reconciliation to source | Silent loss between systems | The warehouse total for the month does not match the ERP’s own report |
Most of these can be written as dbt tests or with open-source tools such as Great Expectations or Soda; the harder part is deciding who receives each alert.
Start with freshness, volume and reconciliation. They catch the failures business users notice first — a dashboard that did not update, a missing day, a total that does not match the ERP — which is exactly the gap Monte Carlo’s survey describes. Add the rest as the pipelines stabilise. Then route every failure to a named person with a runbook, not to a shared inbox. A test that alerts nobody is a log entry.
Eight questions that separate providers who will still be there in year two
Most proposals look alike on the build. They differ on everything that happens after it. Ask these eight in writing, and compare the answers.
- Who owns each pipeline after go-live? A named person or team, not “the client”. If it is you, ask how they will train your people during the build.
- What is the service level? How fresh the data will be, how quickly failures are detected, and how quickly they are fixed — in hours, with the measurement method stated.
- What happens when a source system changes? Vendors upgrade and columns get renamed. Ask how schema changes are detected and who pays to adapt the pipeline.
- Where does the code live, and who owns it? Your repository, your cloud account, your intellectual property — or theirs.
- How is data quality tested, and who sees a failure first? The right answer names the tests, where they run and who gets the alert.
- What will it cost to run? Cloud and people, per month, for the first year.
- How is personal data handled? Masking, access control, retention and where the data is stored — the questions the DPDP Act will put to you, not to them.
- What is the exit plan? Documentation, runbooks and a handover period, written into the contract before the work starts.
A provider who answers all eight clearly has usually run pipelines in production. One who answers only the first three has usually built them and moved on.
Tooling, named honestly
The stack matters less than the discipline around it, but you should know what you are being sold and why. The common choices, by layer:
- Orchestration: Apache Airflow is the default; Dagster is a strong alternative for teams that want data assets modelled explicitly.
- Transformation: dbt, for SQL-based modelling with tests and documentation built in.
- Ingestion: Fivetran for managed connectors priced on usage; Airbyte for an open-source or cloud option with more control and more upkeep.
- Streaming and change-data capture: Apache Kafka, with Debezium for reading changes out of databases.
- Storage and compute: Snowflake, Google BigQuery, Databricks, Amazon Redshift and Microsoft Fabric.
Whatever you choose, two things should not be negotiable: every pipeline and transformation lives in version control with code review, and every business definition — what counts as an active customer, a delinquent loan or a completed order — is written down once and used everywhere. Tools change every few years; definitions that drift between dashboards cost trust permanently.
Two honest notes. A Postgres database with dbt on top can serve a mid-sized company’s reporting for years; a lakehouse earns its cost when you have large volumes, unstructured data or machine learning that needs it. And streaming is only worth paying for when a decision genuinely needs data that is minutes old. Most reporting does not, and daily batch pipelines are cheaper to build and far cheaper to run.
Where data engineering ends and machine learning begins
Data engineering hands machine learning three things: history that is complete enough to learn from, definitions that stay stable (what counts as a customer, a default or a late payment), and features that can be computed the same way in training and in production. When any of the three is missing, the model is built on sand, and it shows up months later as a model that worked in testing and drifts in production.
That is why we test data readiness before anyone builds a platform. A 4-week AI proof of concept answers the question that matters — does the data support this use case — for a fraction of the cost of the full platform, and tells you which of the pipelines above you actually need. The same logic applies to what happens to a model after it ships, which is where most machine learning budgets are underestimated.
If you are earlier than that, our AI consulting work starts with a data-readiness assessment across the use cases you are considering. Built fully custom, or accelerated by our own LLM and inference stack where it speeds delivery — the choice is yours. The data platform is part of our enterprise AI solutions, and it is built to feed whatever you run on top of it, including a private LLM on your own servers.
| Scope the data work before the AI work
A 30-minute call: your source systems, what you want to do with the data, and where the risk is. You leave with an indicative band and the first two things to check. |
Frequently asked questions
What are data engineering services?
Data engineering services connect source systems, build pipelines that move and clean data, and model it into a warehouse or lakehouse that reporting and AI can use. A complete engagement also covers data-quality testing, access and governance, and a handover plan for running the pipelines.
What does a data engineering team do?
It extracts data from operational systems, schedules and monitors the pipelines that move it, models it into usable tables, tests its quality, controls who can access it, and fixes things when a source system changes. In large enterprises more than half of that time goes on maintaining existing pipelines.
What is the difference between data engineering and data science?
Data engineering makes data available and trustworthy; data science uses it to analyse, predict and model. Data science depends on data engineering: models trained on incomplete or unstable data fail in production however good the algorithm is.
How much does data engineering consulting cost in India?
Indicatively about ₹3–13 lakh for one or two sources into an existing warehouse, ₹22–69 lakh for five to ten sources and a new warehouse, and ₹66 lakh to over ₹2 crore for a multi-unit lakehouse, based on India billing rates of $25–49 an hour. Running costs are separate.
Should we outsource data engineering or build in-house?
Many enterprises do both: a consultancy for the design and the build, where experience with many systems saves time, and one or two in-house engineers who are trained during the project and own the pipelines afterwards. Outsourcing the run as well works if the service level and exit terms are written down.
How long does a data engineering project take?
Three to six weeks for one or two sources into an existing warehouse, eight to twelve weeks for a new warehouse with five to ten sources, and four to six months for an enterprise lakehouse. Access approvals and source-system availability are the most common causes of delay.