Data engineering services for one source of truth across sales, marketing and finance

What are data engineering services, in plain words?

Data engineering services are the work of moving data from the tools where it is created into one place where it can be trusted, and keeping that flow running every day. Analysts and dashboards sit on top; data engineering is the plumbing underneath.

In a growing Indian business the plumbing problem looks familiar. Sales numbers live in Tally. Leads live in a CRM. Ad spend lives in Google Ads and Meta Ads Manager. Website behaviour lives in GA4. Online orders live in Shopify or WooCommerce. Every Monday someone exports five files, pastes them into a master sheet and fixes the formulas that broke since last week. The CEO's revenue figure and the finance head's revenue figure differ, and a meeting is spent finding out why.

A data engineer replaces that routine with code. Scheduled jobs pull each source through its API or database connection, land the raw data in a warehouse, clean and reshape it into agreed tables, check the results, and alert someone if anything looks wrong. The payoff is not a prettier chart; it is one number for revenue that everybody stops questioning.

When does a business actually need data engineering services?

You need data engineering when manual reporting takes more than a few hours a week, when teams report different numbers for the same metric, or when you want history your tools do not keep. Before that point, a well-built spreadsheet is often enough, and we will say so.

Clear signals we look for on a first call:

• Month-end reports take days because figures come from four or more systems
• Marketing reports leads and revenue that finance cannot match to invoices
• A key person leaving would take the reporting process with them
• Ad platforms show conversions, but nobody knows which turned into paid invoices
• Sheets hit size limits, slow down or break when someone inserts a column
• You want forecasting or churn prediction, but history is scattered or overwritten

If only one or two reports need speeding up, MIS report automation may solve it faster and cheaper than a full warehouse.

Getting data out of Tally, CRMs, ad platforms and ecommerce stores

Extraction is where most of the engineering time goes, because each source has its own access method, limits and oddities. Well-documented cloud APIs are the easy part; desktop accounting software and custom databases take more care.

Tally is the typical Indian challenge. It usually runs on a desktop or local server, not in the cloud. Tally's own documentation lists JSON and XML exchange over HTTP as integration methods for TallyPrime, so a small agent on the same network can request vouchers, ledgers and stock items on a schedule and push them to the warehouse. We handle multiple companies, financial-year splits and the fact that someone may edit a voucher from three months ago.

ETL or ELT: which pipeline pattern should you choose?

Choose ELT (extract, load, then transform inside the warehouse) for most modern setups, and ETL (transform before loading) when the source data is sensitive, very large or must be cleaned before it can be stored. The difference is where the reshaping happens.

With ELT, raw data lands in the warehouse unchanged, and SQL models turn it into clean tables. The big advantage is traceability: when a number looks wrong, you can compare the reporting table against the untouched raw copy. Changing a business rule means editing a SQL model and rerunning it, not re-extracting months of history. Tools such as dbt make these models versioned and testable.

ETL still has its place. If a source contains personal data you do not want in the warehouse, such as full phone numbers or addresses, we mask or drop those fields before loading. That supports data minimisation under India's Digital Personal Data Protection Act, 2023, though your own counsel should confirm what your obligations are. Very high-volume event data may also be aggregated before loading to control storage costs.

In practice most of our data engineering services projects are hybrid: light cleanup and masking on the way in, heavier business logic in warehouse models.

BigQuery, Postgres or Redshift: choosing a data warehouse

Pick BigQuery when you live in Google's world (GA4, Google Ads, Workspace) and want zero servers to manage; pick PostgreSQL when data volume is modest and you want the lowest predictable running cost; pick Amazon Redshift when your apps already run on AWS. All three work well for small and mid-sized businesses.

BigQuery charges separately for storage and for data processed by queries, and Google's documentation says the free tier includes 1 TiB of query processing each month. Its sandbox gives a lifetime 10 GiB of storage, but tables there expire after 60 days, so it suits trials, not production. GA4's native export to BigQuery is a strong reason to choose it: Google's Analytics help says standard properties can export up to 1 million events per day in the daily export.

Postgres, on a small managed instance, handles tens of millions of rows comfortably for reporting, costs a predictable monthly amount and doubles as the backend for a custom app. Redshift Serverless bills compute per second with a 60-second minimum and storage separately, according to AWS documentation, and AWS recommends setting a maximum RPU-hours limit to avoid surprise bills. We set that limit on day one.

Our PostgreSQL consultant page covers the Postgres route in more depth, including tuning for reporting queries.

Modelling: turning raw tables into one source of truth

Modelling is agreeing what each business word means and encoding it once. Without it, a warehouse is just a bigger pile of exports.

We run short sessions with the people who own each number. Finance defines net revenue: after returns, discounts and GST, booked on invoice date. Sales defines a qualified lead and which CRM stage counts as won. Marketing defines attribution windows. Where definitions conflict, we do not pick a winner; we document both and ask the business owner to choose, then code the result into shared tables such as fact_invoices, fact_ad_spend, dim_customer and dim_product.

We organise the warehouse into layers. Raw holds data exactly as it arrived. Staging cleans types, renames columns and removes duplicates. Marts hold business-ready tables for each team. Dashboards read only from marts. This layering is the single most important habit in data engineering services, because it lets us fix a source problem without touching reports people rely on.

• Customer matching: joining Tally party names, CRM contacts and store buyers by GSTIN, phone or email
• Product matching: one SKU master across Tally stock items, store products and marketplace listings
• Calendar: Indian financial year, festival periods and your own sales weeks

Scheduling, orchestration and monitoring pipelines

Every pipeline runs on a schedule with a clear order, retries on temporary failures and alerts on real ones. A pipeline nobody monitors will fail quietly, and a stale dashboard is worse than no dashboard because people trust it.

For simple setups, scheduled cloud functions or cron jobs on a small server are enough. When there are many dependent steps, for example “load Tally, then CRM, then build the revenue mart, then refresh dashboards”, we use an orchestrator such as Apache Airflow or a lighter alternative, so the order and retries are explicit and visible.

Monitoring covers three questions. Did the job run? Did it load a sensible amount of data? Do the totals still match the source? Answers go to a small status table and to an alert on WhatsApp or email when something fails. Your team gets a runbook explaining what each alert means and what to try first.

Refresh frequency is a cost decision. Hourly refreshes of ad data rarely change a decision; daily is usually enough for finance, and a few operational feeds, such as stock, may justify more frequent loads.

What data-quality checks should a pipeline include?

At minimum: freshness (did today's data arrive), volume (is the row count within a normal range), uniqueness (no duplicate invoices or orders), completeness (key fields not empty) and reconciliation (warehouse totals match the source system). Finance-facing pipelines add stricter reconciliation against Tally day by day.

We write these as automated tests that run after every load. A failed test can block the downstream mart from refreshing, so leadership sees yesterday's correct numbers rather than today's broken ones, with a banner saying data is delayed.

Some checks are specific to Indian businesses. GSTIN formats, state codes that decide IGST versus CGST and SGST, financial-year boundaries in April, and vouchers edited after the books were supposedly closed. A good test suite catches a back-dated Tally entry that changes last quarter's revenue, and tells finance exactly which voucher did it.

The quality checklist table further down lists the tests we set up by default and when each one matters.

How much do data engineering services cost in India?

With BtechWaleTech, a first pipeline pulling two or three sources into a warehouse starts at ₹40,000 (US$600); a multi-source platform with a custom portal or app starts at ₹60,000. Cloud usage is separate and billed to your own account.

The main cost drivers are the number and difficulty of sources, the volume of history to backfill, the strictness of quality checks and whether you need interfaces beyond dashboards. A Google Ads plus GA4 plus Shopify setup on BigQuery is quicker than a Tally plus custom ERP plus three marketplaces setup, even if both end with a similar dashboard.

Running costs for small businesses are often modest. BigQuery's free monthly query allowance covers many small reporting workloads, and a small Postgres instance has a predictable bill. We estimate your monthly cloud usage in the quote, and we design partitions and incremental loads so queries scan only what they need. Market quotes for data engineering services vary widely; compare what is included in testing, documentation and monitoring, not just the build figure.

How long does it take to build a data pipeline?

A first pipeline with two or three sources and a reporting layer usually takes 2–4 weeks; a multi-source platform with finance-grade reconciliation takes 6–12 weeks. Waiting for access to systems is the most common delay.

A typical four-week plan: week one for access, source inventory and definitions sessions; week two for extraction and raw loads, including history backfill; week three for staging and mart models with tests; week four for a parallel run where the old manual report and the new tables are compared line by line until finance signs off.

That parallel run is not optional. It is where we find the credit note that was posted twice in Tally or the ad account nobody told us about. Skipping it is how data projects lose the trust they were meant to create.

Owning your warehouse, code and credentials

Your cloud project, database, code repository and every API credential sit in accounts your business controls. We work inside them with access you grant and can remove.

This matters because a data warehouse becomes infrastructure. Dashboards, forecasts and sometimes customer-facing features come to depend on it. If the warehouse lives in a contractor's personal cloud account, you are one disagreement away from losing your reporting history.

At handover you receive the repository with pipeline code and SQL models, a data dictionary describing every reporting table and column, a diagram of sources and flows, the runbook for alerts and a list of credentials with rotation steps. Free maintenance covers the first two months after go-live; after that, upkeep starts at ₹8,000/mo if you want us to keep watching the jobs. Ownership and support terms are confirmed in your written quote and our terms.

How to choose a data engineering services provider

Choose someone who asks about your definitions and your month-end process before naming tools. Tool-first answers usually lead to a warehouse full of tables nobody trusts.

Questions worth asking any freelancer or vendor: How will you prove the warehouse totals match Tally? What happens when a source API changes its fields? Where will the code live, and can my team read it? What alerts will I get, and who responds? How will you keep monthly cloud costs predictable? Can you show a data dictionary from a sample project, even an anonymised one?

Also be honest about scale. The three of us suit businesses that need a reliable warehouse, a handful of pipelines and good dashboards, without a permanent data team. If you need a dozen engineers building streaming platforms across many business units, a larger team is the better fit.

• Red flag: dashboards promised in week one, before any definitions work
• Red flag: warehouse created in the provider's own cloud account
• Red flag: no plan for testing totals against the source system
• Red flag: vague answers about monthly cloud usage

Security and personal data in a warehouse

Treat the warehouse as a sensitive system, because it gathers customer, sales and financial data that used to be spread across tools. Access should be narrow, logged and removable.

We set up role-based access so marketing sees campaign and lead tables but not salary ledgers, and finance sees invoices but not raw personal fields it does not need. Service accounts get only the permissions each job requires. Personal data such as phone numbers can be hashed where analysis only needs to count or match people. Encryption at rest and in transit is standard on BigQuery, managed Postgres and Redshift.

Compliance itself remains your responsibility, confirmed by your own legal adviser; we build the controls that support it, such as access logs, data minimisation and retention rules that delete old raw data on a schedule.

Worked example: data engineering for a hypothetical Pune distributor

Say a Pune distributor of industrial fasteners runs Tally for billing, Zoho CRM for its sales team, Google Ads for enquiries and a small Shopify store for retail buyers. The owner wants one weekly view of enquiries, quotes, invoices and collections by salesperson and region. This is a hypothetical scenario to show our planning, not a past client.

We would propose Postgres on a small managed instance, because volumes are modest and the same database could later power a salesperson app. Extraction: a Tally agent on the office network pushes vouchers and outstanding bills nightly over Tally's HTTP interface; Zoho CRM and Google Ads come through their APIs; Shopify through its Admin API. Modelling: customers matched by GSTIN where available, otherwise by phone; a salesperson dimension linking CRM owners to Tally cost centres.

Tests would reconcile daily invoice totals to Tally and flag CRM deals marked won with no invoice after 30 days. The first phase, Tally and CRM into a revenue and collections mart, sits near the ₹40,000 starting point; adding ads, Shopify and a sales app moves toward ₹60,000. Dashboards could then be built in Looker Studio at no licence cost.

Data engineering services checklist before you start

Prepare these and the first week of any data engineering project goes twice as fast. Gaps are normal; knowing about them early keeps the estimate honest.

• A list of every system holding sales, marketing, finance or operations data
• Admin or API access for each, or the name of whoever can grant it
• Your current manual reports, with the formulas and sheets behind them
• The five to ten numbers leadership argues about most
• Who owns each definition: revenue, lead, customer, margin
• How much history you need: one financial year, three or everything
• A cloud account preference, or permission for us to create one in your name
• Who will review the parallel run and sign off the numbers

If your Tally data mostly needs to reach a spreadsheet rather than a warehouse, see Tally to Google Sheets first.

Data engineering services across India

We work remotely with businesses in every state. Manufacturers and distributors in Pune, Ahmedabad and Coimbatore usually start with Tally plus CRM, because their sales teams and accounts teams keep separate truths. Software and services firms in Hyderabad and Noida tend to need product usage data joined with billing.

Retail and FMCG distributors in Nagpur and Lucknow want secondary sales by beat and region. Healthcare groups in Chennai and education businesses in Chandigarh combine admissions or appointment systems with ad spend to see real cost per enrolment or patient.

All work happens over calls, screen shares and shared documents in English or Hindi. Where Tally sits on an office machine, your IT person installs the small extraction agent with us on a call; we never need to visit.