14 providers · India

Data Engineering and Analytics Providers in India

The data engineering and analytics market in India serves the country's banking and financial services and IT and ITeS sectors as well as the broader enterprise IT estate concentrated in Bengaluru. Data engineering and analytics providers build the pipelines, warehouses, lakehouses and BI layers that let enterprises move from operational data to decisions. Work spans Snowflake, Databricks, BigQuery and Synapse delivery, real-time streaming, semantic-layer design, and embedded analytics in operational products. TechVendorIndex tracks 14 providers actively delivering data engineering and analytics engagements in India, drawn from global systems integrators, regional champions and specialist boutiques.

About data engineering and analytics in India

Data pipelines, warehousing, bi and analytics consulting. Buyers in India typically engage providers in this category to support transformation work tied to banking and financial services and IT and ITeS priorities, with delivery shaped by local obligations under the Digital Personal Data Protection Act 2023, RBI cyber security framework, SEBI cloud guidelines and CERT-In reporting obligations.

Top data engineering and analytics providers in India

The 14 firms below are ranked by verified delivery presence in India, with focus and rating drawn from TechVendorIndex verified reviews. No vendor pays for placement.

Provider
Focus in Data Engineering and Analytics
Rating
Reviews
Tata Consultancy Services
HQ: Mumbai · Application services and BFSI
Data pipelines, lakehouse and BI
4.1
4,620 reviews
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Infosys
HQ: Bengaluru · Digital, cloud, SAP, Oracle
Data pipelines, lakehouse and BI
4.1
4,180 reviews
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Wipro
HQ: Bengaluru · Engineering and managed services
Data pipelines, lakehouse and BI
3.9
3,540 reviews
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HCLTech
HQ: Noida · Engineering and product services
Data pipelines, lakehouse and BI
4.0
3,120 reviews
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Tech Mahindra
HQ: Pune · Telecom, BPS, network
Data pipelines, lakehouse and BI
3.9
2,680 reviews
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LTIMindtree
HQ: Mumbai · BFSI, cloud, data
Data pipelines, lakehouse and BI
4.0
1,840 reviews
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Persistent Systems
HQ: Pune · Engineering and ISV services
Data pipelines, lakehouse and BI
4.2
1,180 reviews
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Mphasis
HQ: Bengaluru · BFSI and application services
Data pipelines, lakehouse and BI
4.0
980 reviews
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Coforge
HQ: Noida · BFSI, insurance, travel
Data pipelines, lakehouse and BI
4.1
820 reviews
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Hexaware
HQ: Mumbai · BFSI and platform services
Data pipelines, lakehouse and BI
4.0
720 reviews
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Cyient
HQ: Hyderabad · Engineering and geospatial
Data pipelines, lakehouse and BI
4.0
620 reviews
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Birlasoft
HQ: Pune · ERP and application services
Data pipelines, lakehouse and BI
3.9
540 reviews
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Zensar Technologies
HQ: Pune · Application services, digital
Data pipelines, lakehouse and BI
4.0
460 reviews
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Sonata Software
HQ: Bengaluru · Microsoft and platform services
Data pipelines, lakehouse and BI
4.1
380 reviews
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Data Engineering and Analytics market overview in India

Within the broader USD 245 billion enterprise IT services market in India, data engineering and analytics is one of the more active disciplines, growing roughly in line with the 8.4% headline expansion of the wider services market. Demand is concentrated in Bengaluru and Hyderabad, where the largest banking and financial services and IT and ITeS buyers maintain dedicated programme teams. Procurement decisions are shaped by the fact that India is the world's largest IT services delivery base, both as a domestic market and as the offshore hub serving North American and European enterprises. Data platforms in India are consolidating around lakehouse architectures (Databricks and Snowflake) with reverse-ETL into operational systems. AI workloads have made data quality and lineage governance the primary investment focus rather than reporting BI. Mid-market buyers in India increasingly favour specialist firms with deep domain expertise over generalist consultancies, while the largest programmes continue to be awarded to the multinational integrators with global delivery models and embedded banking and financial services practices.

How to select a data engineering and analytics provider in India

Use the following criteria to shortlist providers before issuing a formal request for proposal. Most procurement teams in India weight references and operating-model fit more heavily than headline rate cards.

Typical engagement model

Platform foundation work typically runs three to six months at USD 500,000 to USD 2M. Steady-state data engineering pods cost USD 35,000 to USD 80,000 per month depending on seniority and location. Major migrations from legacy warehouses (Teradata, Netezza) extend to 12 to 18 months.

Pricing should always be benchmarked against at least three references in India at comparable scope. Engage independent advisory support before signing multi-year contracts above USD 5M annual contract value.

Related categories and regions

Compare the data engineering and analytics market in India with other service lines in the same country, or with data engineering and analytics in other markets covered by TechVendorIndex.

Frequently asked questions

Snowflake or Databricks in India?
Snowflake is most often selected by buyers prioritising SQL workloads and data sharing with partners. Databricks is selected when machine-learning and data engineering converge on the same platform. Many enterprises in India run both.
How do we improve data quality in India?
Establish data product ownership at the source-system level, deploy automated quality monitoring, define SLAs for produced datasets, and treat data contracts between teams as first-class artefacts. Tooling alone does not solve organisational gaps.
Is generative AI changing data engineering priorities in India?
Yes — RAG and agent workloads have raised the cost of poor data lineage and made unstructured-data pipelines a first-class concern. Buyers in India are increasingly investing in vector search and document chunking pipelines.
How do we measure the ROI of a data programme in India?
Tie every data product to a named business decision or operational process, track decision quality and cycle time rather than dashboard adoption, and review the data product portfolio annually against business outcomes.
Last updated: May 2026
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