Reliable & consistent data
One source of truth across the organization — trust, not reconciliation.
Solutions · Enterprise Data
Data engineering services: design, build and operate modern data platforms
We help enterprises build data systems that are reliable, scalable and AI-ready — enabling seamless data flow from creation to analytics and decision-making.
01 · What we do
Enterprise data is the foundation for AI, analytics, and modern applications.
We design and build end-to-end data platforms that manage the full lifecycle — from creation and ingestion to engineering, storage, and analytics.
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Reliable data pipelines
One model for ingestion across the enterprise — not fragmented connectors.
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Scalable platforms
Lakehouse and warehouse architectures that grow without siloed rebuilds.
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Ready for AI & analytics
Structured, governed datasets — not just storage with a query engine.
02 · Data lifecycle · 05
End-to-end. From creation to consumption.
We support the full enterprise data lifecycle — five stages, one execution model. Each stage plugs into the next, with quality, governance and observability built in.
- /01
Data Creation & Ingestion
Capture and integrate data from across the enterprise.
- Structured and unstructured data sources
- Real-time and batch ingestion
- API and system integrations
- Data quality and validation
FocusEnsure consistent, reliable data collection across all sources.
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Data Engineering
Build pipelines and processes to transform and prepare data.
- Data pipelines and workflows
- Data transformation and standardization
- Data enrichment and processing
- Data quality management
FocusConvert raw data into structured, usable datasets.
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Data Platforms & Warehousing
Design scalable systems to store and organize data.
- Data lakes and lakehouse architectures
- Enterprise data warehouses
- Data modeling and schema design
- Master data management
FocusCreate structured, scalable platforms for enterprise-wide data access.
- /04
Analytics & Consumption
Enable data access, insights, and decision-making.
- Data APIs and data services
- Analytics and reporting
- Business intelligence dashboards
- Self-service data access
FocusTurn data into actionable insights across teams.
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Data Operations & Optimization
Manage, monitor, and continuously improve data systems.
- Data platform monitoring
- Performance optimization
- Cost management
- Data governance and security
FocusKeep data systems reliable, efficient, and governed at scale.
03 · Foundations
Strong data systems require strong foundations.
Strategy, governance, security and quality run as a horizontal layer across every stage of the data lifecycle — establishing trust, consistency and scalability across all data systems.
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Data strategy & architecture
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Data governance & access control
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Security & compliance
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Data quality & lifecycle management
04 · How we deliver
A structured approach to building data platforms.
Five movements that take a data platform from audit to optimization — repeatable across business units, but tuned to each one’s data gravity, source systems and consumption patterns.
01
Assess
Audit the current data landscape and architecture.
02
Design
Define a scalable data strategy — lakehouse, warehouse, mesh.
03
Build
Stand up pipelines and platforms; integrate with source systems.
04
Enable
Wire up analytics, APIs and consumption layers for teams.
05
Optimize
Monitor, govern and continuously tune performance and cost.
Where this connects · Jo Platform
Enterprise data is a critical input for AI execution.
Jo integrates with enterprise data platforms to enable seamless, governed access to data for AI systems.
- Structured access to enterprise datasets
- Consistent data usage across AI workflows
- Scalable integration between data and AI systems
05 · Outcomes
Data that moves the business.
Every Enterprise Data engagement is anchored to one of these five outcomes — and tracked against it after go-live.
Faster access to insights
Time-to-decision drops from weeks to hours with self-service analytics.
AI-ready platforms
Datasets, schemas and governance built for AI integration on day one.
Improved data quality
Validation, lineage and governance baked into every pipeline.
Lower operational complexity
Consolidated platforms cut tool sprawl, headcount load, and unit cost.
FAQ
Frequently asked questions
A modern enterprise data platform is a cloud-native foundation that moves data reliably from source systems to analytics, AI and decision-making — including ingestion, transformation, governance, observability and access. It is what makes enterprise AI possible at scale.
We design, build and run modern data platforms on Databricks, Google Cloud, Microsoft Azure and AWS, using dbt for transformation and the Jo platform for governance and AI-readiness. Every platform is designed to be reliable, scalable and AI-ready.
AI-ready means your data is integrated, governed, well-modeled, observable, and exposed through interfaces that AI systems (and the teams that build them) can consume safely — semantic layers, feature stores, vector indexes, contracts and access controls.
Databricks, Google BigQuery, Snowflake, Microsoft Fabric and others — alongside dbt, Looker, MicroStrategy and the AI tooling on top. Dataflix is a Databricks partner, a Google Cloud Sell-Service-Build partner and a dbt Labs partner.
Every Dataflix AI engagement runs on top of a clear data foundation. We typically combine a data-platform workstream with an AI-platform workstream on Jo, so AI pilots are not blocked on missing or unreliable data.
Foundational data-platform engagements run in parallel with the AI Execution Program and deliver initial value in 8–12 weeks, with a multi-quarter roadmap to broaden coverage and ownership.
Yes. As a Databricks partner, Dataflix delivers lakehouse architecture, data pipelines, Unity Catalog governance and Marketplace integrations, from design through production operations.
Yes. We build on Snowflake for cloud data warehousing, data engineering and Snowpark workloads. We help design, migrate and operate governed data platforms.
Yes. We deliver Power BI and Microsoft Fabric semantic models and dashboards, connecting business metrics to governed data pipelines and access controls.
Yes. Dataflix builds dedicated data engineering teams in India and Mexico through its managed GCC model, with delivery governance and a Build-Operate-Transfer option.
Build a platform that supports enterprise AIModern data,
Modern data,
ready for what’s next.
Platforms we build on
Databricks
As a Databricks partner, we build lakehouse platforms, Unity Catalog governance and Marketplace integrations.
Our Databricks partnershipSnowflake
We build on Snowflake for cloud warehousing, governed data sharing and Snowpark engineering workloads.
Microsoft Fabric & Power BI
We deliver Power BI and Fabric semantic models and dashboards, connecting business metrics to governed enterprise data.
Google BigQuery
We deliver cloud analytics and AI-ready data foundations on BigQuery.
Our Google Cloud practicedbt
As a dbt Labs partner, we build tested, documented data transformations and analytics engineering workflows.
Our dbt Labs partnershipLooker
As a Looker partner, we deliver governed business metrics, embedded analytics and self-service exploration.
Our Looker partnershipFivetran & Boomi
We connect enterprise sources with Fivetran ingestion and Boomi integration workflows to keep operational and analytical data moving.
Data engineering talent
Dedicated data engineers in India and Mexico
Build a dedicated team that owns your pipelines, data quality and platform operations. Mexico supports time-zone overlap with US teams, while India offers cost-efficient access to engineering talent. Hire dedicated data engineers through our managed GCC model.
Business intelligence & analytics
We deliver governed analytics with Looker, Power BI and MicroStrategy, connecting reliable data to actionable dashboards and business metrics. CIO Review recognized Dataflix among its Top 10 Data Analytics Solutions Providers 2022.