DATA SOLUTIONS COMPANY
Build the data foundation everything else depends on — modern warehouses, lakehouses, and pipelines on Snowflake, Databricks, BigQuery, and Redshift, engineered for analytics, AI, and decisions that need to be right the first time.
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Plan My Data ProjectWhy Modern Data Platforms Are a Competitive Advantage
Every meaningful business decision now runs on data — and every meaningful AI capability runs on data twice over. Companies with modern data foundations make better decisions faster, ship AI products in months instead of years, and outpace competitors who are still wrestling with brittle pipelines, conflicting metrics, and dashboards that nobody trusts. Here's why founders, operators, and enterprise teams are investing in serious data infrastructure in 2026:
AI Lives or Dies on Data Quality
Your AI roadmap depends entirely on whether your data is reliable, well-modeled, and well-governed. Companies with mature data platforms ship AI products 3 to 5x faster — because the hardest part of an AI project isn't the model, it's the data underneath.
Cloud Warehouses Changed Economics
Snowflake, Databricks, BigQuery, and Redshift made enterprise-grade analytics affordable for any company, not just Fortune 500. Modern data platforms scale automatically, separate compute from storage, and bill by query — making good data infrastructure radically cheaper than it was five years ago.
ELT Beats ETL Most Times
Old-world ETL pipelines were expensive, brittle, and slow to change. Modern ELT — using Fivetran, Airbyte, dbt, and similar tools — pulls data first, transforms in the warehouse, and adapts to schema change without breaking. The shift is structural, not cosmetic.
Lakehouses Resolve Old Tradeoffs
You no longer have to choose between a warehouse (great for SQL) and a lake (great for unstructured data and ML). Modern lakehouses on Databricks, Snowflake, and Iceberg deliver both — supporting analytics, ML training, and real-time use cases on one foundation.
Real-Time Is the New Default
Yesterday's data is no longer enough. Streaming platforms like Kafka, Kinesis, and Confluent — combined with materialized views, change data capture, and reverse ETL — make real-time analytics and operational AI tractable for the first time.
Bad Data Compounds Quietly
A pipeline that silently drops rows. A dashboard that shows the wrong number. A model trained on stale data. Bad data accumulates compounding errors throughout the business — and the cost is rarely caught until a major decision goes sideways.
Ready to build the data platform AI demands?
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Why Most Data Engagements Fall Short
Big-4 firms ship 24-month enterprise data programs priced for procurement budgets — and deliver rigid architectures that don't adapt as the business evolves. Generic offshore shops build pipelines that work on day one and break on day 90 because nobody thought about schema drift. Internal teams duct-tape together stacks of Airflow, custom Python, and CSV exports that work until someone leaves and nobody can read the code. Vendors push their own platforms regardless of fit, leading to expensive lock-in or platform sprawl. Six months in, the warehouse exists but the data nobody trusts is still in spreadsheets. Three different teams report three different revenue numbers because metrics aren't centrally defined. The AI team can't ship because the training data has integrity issues nobody noticed. The data engineer who built half the pipelines moved on, and onboarding the next engineer takes weeks. The investment is real — the trust isn't. That's where ZAPTA steps in — senior data engineers and platform architects who treat data infrastructure as production-grade engineering, AI-augmented pipeline development, vendor-neutral architecture decisions, and engagement structures that deliver shippable value every wave instead of multi-year megaprojects.
Who we help most:
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founders building their first real data platform
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scaling startups outgrowing CSV exports and ad-hoc queries
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AI teams whose models depend on trustworthy data
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enterprises modernizing legacy data warehouses
How ZAPTA Delivers Data Solutions
We offer four main ways to help — pick the one that matches the data challenge you need to solve:
Greenfield Data Platforms
You're building your data foundation from scratch — or replacing a duct-taped first version with something durable. We design and build modern data platforms on Snowflake, Databricks, BigQuery, or Redshift — including ingestion, transformation, modeling, and access layers. Most greenfield platforms ship in 8 to 16 weeks.
Data Warehouse / Lakehouse Modernization
Your current data warehouse — Teradata, Oracle, on-prem Hadoop, legacy SQL Server — has hit scale, cost, or capability limits. We modernize to a cloud-native platform with proper migration discipline, parallel running, and validated cutover. Most modernization programs run 12 to 24 weeks.
ELT Pipeline & Data Engineering
You have a platform but need pipelines built — bringing data from your SaaS apps, transactional databases, event streams, and external sources into the warehouse, modeled cleanly with dbt or equivalent transformation tools. Most pipeline engagements run 4 to 12 weeks depending on source count and complexity.
AI-Ready Data Foundations
Your data platform needs to support AI and ML — feature stores, vector databases, training data pipelines, model observability, and AI-grade data governance. We design and implement AI-ready data foundations integrated with your existing platform. Most AI-readiness engagements run 6 to 12 weeks.
Not sure which path fits your situation?
Tell us where you are and we'll recommend the right approach — honestly.
Get a Free ConsultationFlexible Ways to Work With ZAPTA
Every data platform project is different — so is every team's budget, scale, and operational maturity. Choose the engagement model that matches how you want to work.
Fixed-cost data platform sprints
Ideal for teams with clear scope — first warehouse build, defined pipeline package, or focused modernization. We scope, design, and deliver against fixed pricing — including platform setup, ingestion, modeling, and observability. Perfect for first data platforms, focused pipeline projects, and bounded modernization work.
Best for:
Founders building first platforms, SMEs running discrete data projects, fixed-budget enterprise pilots.
Wave-Based Data Programs
The default option for organizations building durable data platforms or modernizing at scale. We organize programs into 4 to 8-week waves — each shipping production value (a working pipeline, a usable model, an AI-ready foundation) — building organizational momentum and compounding investment value across the program.
Best for:
Most enterprise data programs covering platform build, modernization, and AI-readiness work.
Embedded Data Engineering Squad
A long-term engagement where we embed senior data engineers, analytics engineers, and a delivery lead into your team — running ongoing pipeline development, modeling, observability, AI-data work, and continuous improvement. Continuity, compounding domain knowledge, and senior expertise without the permanent hire.
Best for:
Enterprises and scale-ups running ongoing data platform programs at portfolio scale.
Custom Quotations
Multi-region data platforms, regulated industries, AI-heavy data foundations, real-time streaming requirements, M&A data consolidation, or unusual technical constraints — we build a tailored quotation around your exact situation. Tell us the program, the scale, and the outcome you need. We respond within 24 hours.
Best for:
Enterprise, regulated, or non-standard data platform engagements that don't fit a template.
Which engagement model is right for you?
Share your data platform details and get a tailored recommendation within 24 hours.
Request a Tailored QuoteSigns You Need a Data Solutions Partner
If any of these sound familiar, it's time to bring in senior data engineering expertise:
Three teams in the company report three different numbers for the same metric — and nobody can authoritatively say which is right
Your data team's busiest activity is firefighting broken pipelines, not building new analytics or supporting AI.
You're shipping AI products but your training data is questionable, your evaluation data is incomplete, and your model observability is non-existent.
Your existing data warehouse — Teradata, Oracle, legacy SQL Server, or on-prem Hadoop — has become slow, expensive, or both.
Your stack is duct-taped Airflow + Python + CSV exports — and onboarding a new engineer takes weeks because nothing is documented.
Your CFO and your COO can't agree on which dashboard tells the truth — and the disagreement keeps recurring.
You want real-time analytics or operational AI, but everything you have is batched daily or worse.
Recognize yourself in any of these?
Get a free 30-minute data platform diagnostic from a senior architect.
Data Solutions Services We Offer
A complete data engineering practice covering platform build, pipelines, modeling, and AI-ready foundations — from greenfield to enterprise modernization:
Modern Data Platform Architecture
Vendor-neutral architecture for cloud data platforms — Snowflake, Databricks, BigQuery, Redshift — covering ingestion, storage, transformation, and access layers.
Data Warehouse Implementation
Production-grade data warehouse build on Snowflake, BigQuery, Redshift, or Synapse — including dimensional modeling, performance tuning, and access governance.
Data Lakehouse Architecture
Lakehouse implementation on Databricks, Snowflake, or open-table formats (Iceberg, Delta Lake, Hudi) — supporting analytics, ML, and real-time on one foundation.
ELT Pipeline Engineering
Modern ELT pipelines using Fivetran, Airbyte, Stitch, and custom ingestion — with dbt-based transformations, testing, and CI/CD.
Data Modeling & dbt
Dimensional modeling, data vault, and dbt-based transformation development — turning raw data into analytics-ready models with tests, documentation, and lineage.
Real-Time Data Streaming
Streaming platforms with Kafka, Kinesis, Confluent, and Flink — including change data capture, stream processing, and real-time materialized views.
Data Migration & Modernization
Migration from legacy warehouses (Teradata, Oracle, on-prem Hadoop) to cloud-native platforms with parallel running, validation, and rollback discipline.
Reverse ETL & Operational Analytics
Reverse ETL with Hightouch, Census, and Workato to push warehouse data back into operational systems — making data actionable inside CRM, marketing, and support tools.
AI / ML Data Foundations
Feature stores, vector databases (Pinecone, Weaviate, Qdrant), training data pipelines, evaluation datasets, and ML observability.
Data Quality & Observability
Data quality monitoring with Monte Carlo, Bigeye, Soda, and dbt tests — surfacing incidents before they reach dashboards or models.
Master Data Management (MDM)
MDM implementation including entity resolution, golden records, and reference data management for organizations consolidating customer, product, or supplier data.
Data Platform Operations
Ongoing data platform stewardship — pipeline monitoring, cost optimization, performance tuning, and continuous improvement.
Need a data service you don't see listed?
We design custom engagements for unique data environments and regulated industries.
Discuss Your ProjectHow Our Data Solutions Process Works
Every data engagement follows a clear three-phase lifecycle, broken into wave-based execution underneath. Greenfield platforms typically run 8 to 16 weeks. Modernization programs run 12 to 24 weeks. Pipeline engagements run 4 to 12 weeks. AI-readiness engagements run 6 to 12 weeks. Embedded squads run continuously.
Architect and Plan
- Discovery workshops with engineering, analytics, finance, AI, and business stakeholders.
- Source system mapping — current databases, SaaS apps, event streams, and external data feeds.
- Use-case prioritization — which dashboards, models, AI products, and decisions need data first.
Discovery, source mapping, architecture, platform selection, modeling design.
Build and Ship
- ELT pipeline development using Fivetran, Airbyte, custom code, or hybrid approaches.
- dbt-based transformation development with tests, documentation, and lineage from day one.
- dbt-based transformation development with tests, documentation, and lineage from day one.
Platform setup, ingestion, transformation, modeling, observability rollout.
Operate and Scale
- Governance setup — naming standards, deployment pipelines, access controls, lineage tracking.
- Governance setup — naming standards, deployment pipelines, access controls, lineage tracking.
- Governance setup — naming standards, deployment pipelines, access controls, lineage tracking.
Production rollout, governance, cost optimization, ongoing improvement.
Want this process for your data platform?
Tell us about your data environment and get a tailored platform roadmap within 24 hours.
Start Your EngagementTools We Use for Data Solutions Projects
Our data toolkit combines proven cloud platforms, modern data engineering frameworks, and AI-augmented development — chosen for the specific use case, not vendor partnerships.
Building AI-Native Products
Transform your ideas into intelligent digital products with AI at the core. Our AI-native engineering approach combines human expertise with advanced AI tools to deliver scalable, secure, and high-quality software while reducing cost and accelerating innovation.
Talk to Our AI Experts
Real Data Engagements We've Delivered
Real scenarios where founders, operators, and enterprise teams brought us in to ship data platforms that compound value:
B2B SaaS platform from zero
Founder with a clear vision shipped a multi-tenant SaaS platform in 12 weeks auth, billing, dashboards, and core workflows. Live customers within 90 days.
Real Estate Fintech
How ZAPTA delivered a secure digital-identity and contact-management mobile app that keeps users’ details verified and up to date in real time, launched across Denmark and the USA with 5,000+ verified users
Enterprise legacy modernization
Rebuilt a Fortune 500 team's internal operations system migrated from legacy stack to cloud-native in 16 weeks with zero downtime during cutover.
Content Platform Redesign
How ZAPTA helped redesign and rebuild the V3 experience for a leading content-repurposing platform, bringing clarity, consistency, and a unified design system across every module of a product trusted by 980K+ creators.
Digital Identity Verification App
How ZAPTA delivered a secure digital-identity and contact-management mobile app that keeps users’ details verified and up to date in real time, launched across Denmark and the USA with 5,000+ verified users
FinTech Trade & Financing Platform
Founder with a clear vision shipped a multi-tenant SaaS platform in 12 weeks auth, billing, dashboards, and core workflows. Live customers within 90 days.
Healthcare Onboarding Platform
How ZAPTA helped a healthcare organization replace a manual, fragmented hiring process with a unified, compliance-ready onboarding platform, bringing applicants, employees, referees, and administrators into a single role-based system.
Property Management Platform
How ZAPTA helped a property-management company replace fragmented manual operations with a single platform connecting tenants, vendors, and property owners, with 20,000+ properties listed across 10 US states.
Smart POS Platform
How ZAPTA helped a technology company build a SaaS point-of-sale platform that unifies sales, inventory, and payments with real-time analytics and full online–offline functionality — built for the Saudi market across four sectors.
Ticketing Analytics Platform
How ZAPTA built a real-time analytics and ticketing-insights platform that reveals pricing trends and optimal purchase timing, helping buyers across 100+ locations purchase 15K+ tickets and save over $100K.
Unified GRC Platform
How ZAPTA helped deliver a unified governance, risk, and compliance platform that automates compliance, risk, and legislative tracking — cutting regulatory-change monitoring time by 40% and audit preparation by 35%.
AI EdTech Platform
How ZAPTA helped an EdTech client turn traditional tutoring into a personalized, AI-driven experience, intelligently matching students with suitable tutors, with 1,500 students enrolled and 591+ expert tutors on the platform.
What You Get When You Work With ZAPTA
Every data engagement ships with production-ready outputs your team owns long-term:
Data platform architecture document with explicit rationale for vendor and design choices.
Source-system mapping covering databases, SaaS apps, event streams, and external feeds.
Production-grade data warehouse or lakehouse on Snowflake, Databricks, BigQuery, or Redshift.
ELT pipelines using Fivetran, Airbyte, custom code, or hybrid approaches with monitoring built in.
dbt project with models, tests, documentation, and lineage from day one.
Dimensional models, data vault, or use-case-aligned modeling for analytics-ready data.
Real-time streaming pipelines (where applicable) using Kafka, Kinesis, or Confluent.
Data quality monitoring with Monte Carlo, Bigeye, Soda, or dbt-test-based observability.
AI / ML data foundations — feature stores, vector databases, training pipelines (where applicable).
Infrastructure-as-code (Terraform or Pulumi) for the entire data platform.
Living documentation — architecture diagrams, dbt docs, runbooks, and operator training.
Optional retainer for ongoing data platform operations and continuous improvement.
Ready to see these deliverables for your data platform?
Book a scoping call and receive a full deliverable list within 24 hours.
Book Your Scoping CallWhy Founders and Teams Choose ZAPTA
Many companies offer data services. Here's what makes ZAPTA a specialist data solutions partner:
Senior Data Engineers Only
Your engagement is led by senior data engineers, analytics engineers, and platform architects — not juniors learning on your time and budget. The same people who design the platform also build it and stand behind it post-deployment.
Vendor-Neutral by Design
We don't carry vendor partnerships that bias our recommendations. Snowflake, Databricks, BigQuery, Redshift, hybrid — we recommend what's right for your scale, workload, and budget, not what's best for our partner program.
AI-Ready From Day One
Every data platform we build is designed with AI use cases in mind — clean lineage, well-modeled data, feature-store integration, vector capabilities. AI ambitions don't get blocked by data foundations we built.
Production Discipline on Day One
We treat data platforms as production engineering — version control, CI/CD, tests, documentation, observability — from the first commit. The result is platforms that scale and adapt instead of becoming brittle within a quarter.
Industries We Build Data Platforms For
Sectors where data platforms translate directly into competitive advantage and operational margin.
Fintech
Real Estate & Construction
Healthcare
Technology
Education
Retail
Insurance
Compliance & Governance
Transportation & Logistics
Energy Beyond Data Solutions — Full-Stack Services
Data platforms are one part of a complete data strategy. ZAPTA is a complete technology company — we design, build, and scale the full stack alongside your data program so you can ship a complete data operation, not just a warehouse.
Data Solutions FAQs
Structured for AI search engines (ChatGPT, Gemini, Perplexity, Claude) and Google rich results. Implement FAQPage JSON-LD for every question.
Data Solutions builds the platform — warehouses, lakehouses, pipelines, and the data infrastructure that everything else runs on. Data Security & Compliance protects what's in the platform (classification, DLP, masking, governance). Data Analysis surfaces insights from the platform (dashboards, BI, embedded analytics). Predictive Insights builds ML models on the platform. Most enterprises need all four — and they're frequently delivered as paired engagements.
Greenfield data platforms typically run 8 to 16 weeks. Modernization programs run 12 to 24 weeks. Pipeline engagements run 4 to 12 weeks. AI-readiness engagements run 6 to 12 weeks. Embedded squads run continuously. We commit to fixed wave dates during scoping so you can plan around them.
Pricing depends on platform choice, source count, modeling complexity, AI requirements, and engagement model. We offer fixed-cost sprints, wave-based programs, embedded squads, and custom quotations. Most data platforms pay for themselves within 6 to 18 months through faster decisions, AI-product enablement, and operational efficiency. Book a call for a tailored quote within 24 hours.
Four primary models: Fixed-Cost Data Platform Sprints for defined scope, Wave-Based Data Programs for multi-quarter buildouts, Embedded Data Engineering Squads for ongoing programs, and Custom Quotations for regulated or non-standard work. We'll recommend the right fit during discovery.
Vendor-neutral by design — Snowflake, Databricks, BigQuery, Redshift, Synapse, and hybrid combinations. We pick the platform based on your scale, workload mix (analytics vs. ML), pricing model, and existing cloud commitments. Most engagements include explicit platform-fit analysis during discovery if a target hasn't been chosen.
Yes. Migrations from Teradata, Oracle, on-prem Hadoop, legacy SQL Server, and similar legacy platforms are core specialties. We handle source assessment, target architecture, parallel running, validated cutover, and decommissioning — preserving data integrity and minimizing business disruption. Most modernization programs run 12 to 24 weeks.
Yes. Real-time platforms with Kafka, Kinesis, Confluent, and Flink — including change data capture, stream processing, and real-time materialized views — are core capabilities. We design real-time architectures for operational analytics, AI inference pipelines, and real-time personalization.
Yes. We build AI-ready data foundations including feature stores, vector databases (Pinecone, Weaviate, Qdrant), training data pipelines, evaluation datasets, and ML observability. AI-readiness is a specialty — and most data foundations we build are designed with AI use cases in mind from day one.
Data observability is built into every engagement. We deploy Monte Carlo, Bigeye, Soda, or dbt-test-based observability with anomaly detection, schema-change alerts, and incident management. Data quality monitoring isn't an add-on — it's part of the platform from day one.
Yes. You own everything — platform configurations, dbt projects, pipeline code, IaC modules, observability dashboards, runbooks, and all deliverables. Full IP assignment is signed before kickoff. No lock-in, no licensing beyond underlying platform fees, no dependency on us going forward.
Yes. Embedded data engineering squads and platform retainers cover ongoing pipeline development, modeling, observability, AI-data work, and continuous improvement — same senior team every month, predictable pricing, SLA-backed response. Common for teams running enterprise data platforms at scale.
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