Service · Remote-first senior specialists
Get dependable data from the systems you already use. MV.tech builds and improves ingestion pipelines, cloud warehouses on BigQuery, Snowflake, Redshift or PostgreSQL, dimensional and semantic models, and the validation that keeps reporting trustworthy — including migrations off legacy databases and manual spreadsheets.
The starting point
What we deliver
Bring data from APIs, files and databases into a repeatable ingestion process in Python and SQL. Define incremental loads, retries, scheduled refreshes, automated backfills and duplicate handling around the behaviour of each source.
Design BigQuery, Snowflake, Redshift or PostgreSQL tables and transformations across raw, staging, curated and reporting layers. Make business definitions, table ownership and refresh dependencies explicit before expanding the platform.
Move legacy databases, data lakes and manual reporting onto a cloud warehouse. Start with database discovery and metadata profiling — catalogue queries, DDL and information schema — then produce detailed source-to-target mappings, ingestion plans through tools such as Azure Data Factory and Blob Storage, and the upstream and downstream dependency analysis that shows what a change will break.
Build fact and dimension models with an agreed grain, conformed dimensions, bridge tables and source-load tracking, then the curated views and semantic structures that BI tools read. Review existing Power BI, SSAS or Analysis Services models before rebuilding them, and handle Salesforce CRM Analytics datasets, recipes, dataflows and field mapping where reporting already lives there.
Check freshness, completeness and reconciliation at meaningful boundaries: record counts, date coverage, nulls, duplicates, totals and metric comparisons between pipeline stages. Add outlier detection, useful logs and failure alerts so a successful job is not mistaken for trustworthy data.
Reduce what a query has to read. Reusable transformation layers, caching, parallel reads, partitioning and incremental Parquet extracts are the usual levers, with the before-and-after measured on your own queries and refresh windows.
Consolidate cross-chain transaction data into a warehouse for teams that need it: raw transactions, logs and events decoded with contract ABIs and Web3.py, FX reference tables, token-level valuation, fee proration and staking-reward reconciliation across networks.
Connect validated datasets to Power BI, Tableau, Looker Studio, Streamlit or downstream applications. Include source mappings, ER diagrams, validation checklists, operating notes and recovery steps in the agreed handover.
A useful first project
Start with a business report that matters and trace its path back to the source. Agree which records belong in the result, how fresh they need to be and how correctness will be checked — record counts, totals and a comparison against the numbers people trust today. Then scope the ingestion, transformations, model and reporting output together. This gives the project a concrete acceptance test before more sources, a full migration or additional dashboards are added.
See our delivery processWorking together
Profile the databases and APIs involved, review access, limits, volumes and history, and agree the business meaning of each metric.
Implement the pipeline and model, then compare output with source records: counts, totals, date coverage, empty periods, updates and retries.
Document schedules, lineage, ownership and recovery. Agree support and the next useful source, model or reporting layer.
Teams our engineers have worked with
Before we begin
Not answered here? Ask us directly or read the full FAQ.
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.
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.
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.
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.
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.
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Buyer's checklist
Who does the work, how the first milestone is scoped and how correctness is proven.
Buyer guide
How to evaluate a data consultancy for offshore delivery, and where MV.tech fits.
Remote delivery
Realistic overlap windows and the async habits that keep distributed work moving.
Your next step
Bring your goal, current tools and preferred working hours. We use the first 30-minute conversation to clarify fit and an initial scope, and you leave with a written next step.
Remote from Ahmedabad, India · Overlap with any timezone · No obligation
