Delivery Plan · Core
Now $4,500£3,750R79,000/ monthWas $8,000£6,700R140,000
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Data Engineering on a Monthly Delivery Plan
Get reliable data pipelines and dashboards that your teams trust for decisions, on Databricks, Snowflake or your own stack. Through the Software Factory Delivery Plan, you get one team, one predictable monthly price and one prioritised backlog. Add mobile, web, backend, AI, QA or DevOps work to the same backlog whenever you need it.
Delivering since 201248 hours to first codeManaged live on Scrums.com
Use your plan for data engineering and analytics.
14DAYSFirst deliverable in 14 days or your first month is free.
ThousandsOf companies since 2012
Trusted by engineering leaders at ambitious startups, enterprises and global brands.
Read client stories →
How your Software Factory Delivery Plan works
How the data engineering and analytics process works
- 01Queue your data workPick data scopes or describe your own.
- 02SubscribeCheck out and onboarding starts immediately.
- 03First code in production in 48 hoursAccess to your data sources, cloud and repos.
- 04Build, test, repeatTrack every pipeline run on the Scrums.com Platform.
Scrums.com Platform
Track every pipeline, model and dashboard on the Scrums.com Platform
Goals break down into tasks. Milestones are agreed before work begins. You track every pipeline, model and dashboard from source to production, and the next task starts the moment one ships.
Requirement9
CDC ingestion from the orders database
Data5 pts
dbt models for revenue and customers
Data5 pts
CI/CD for dbt and Airflow changes
DevOps3 pts
Semantic layer for the revenue metrics
Data3 pts
Delivering1
Kafka events stream to the lakehouseData
- Models and tests written
- Pipeline runs green in CI
- Code review approved
- Pipeline live in production
Data engPR #1820
Gate
Live38
Quality checks on the curated tables
Data · just nowAirflow DAGs for the nightly loads
Data · 4d agoFeature table for churn signals
AI · 6d agoExecutive KPI dashboard v1
Data · 8d agoIllustrative demo with sample data. On the Scrums.com Platform, the board shows your own goals, tasks and releases.
Scope and capacity
What one workstream delivers, for data or any software
Your backlog can hold any number of requests, but the team delivers them one workstream at a time. A workstream is one managed team working on your highest-priority request until it is live, then starting the next. More requests mean more months, not faster delivery. Work is scoped and delivered within your plan’s capacity. Priorities can change; additional parallel workstreams require more capacity. The same plan covers mobile, web, backend, AI, QA and DevOps work, so all of it can share one backlog.
- Active at once
- One request
- First use case live
- 4 to 6 weeks
- Basic platform
- 1 to 2 months
- Data owned by
- You
Example: a data platform on one workstream
Illustrative- Month 1Data landscape assessment and roadmap, then the foundation: platform provisioned, storage and orchestration set up, governance tooling configured, and the first pipelines built for priority sources.
- Month 2The first analytics use case goes live with quality validation and monitoring. More sources are integrated and historical data is backfilled.
- Month 3Streaming pipelines, a semantic layer and a metrics framework. BI tools deployed, self-service analytics in place and users trained.
A sample platform programme. It matches the 4 to 6 weeks to the first analytics use case and the 1 to 2 months a basic platform typically takes; a standard lakehouse takes 3 to 4 months. Onboarding starts immediately after checkout, and your delivery owner scopes your own plan with you.
What affects the number of months
- Number of sourcesEach database, SaaS tool, event stream or file source adds integration work.
- Starting pointSpreadsheets and manual exports need a platform first; an existing warehouse can be extended or migrated.
- Batch or real timeStreaming pipelines, change data capture and real-time aggregations add work over batch loads.
- Governance and complianceCatalogs, lineage, access controls and GDPR, CCPA or SOC 2 frameworks add work up front.
- Analytics and ML scopeSemantic layers, BI rollout, feature stores and model serving each add work.
- Review and approvalsHow quickly your team agrees metric definitions and signs off releases.
Capabilities
Data pipeline development, platforms, governance, ML and analytics.
Enterprise data platform architecture
Databricks · Snowflake · AWS · Azure · GCPModern data platforms on Databricks Lakehouse, Snowflake or cloud-native data services. Warehouses, data lakes and streaming infrastructure consolidated into one platform for SQL analytics, Python/Spark processing and ML workloads.
When it fitsYour data is siloed across systems, and consolidating it for analytics needs manual exports or complex integrations.
Trade-offsA warehouse migration runs in parallel with the old system and moves workloads in phases. Most migrations take 6 to 12 months.
How the plan covers it
Queue this scope →Lakehouse architecture consolidating warehouses, lakes and streaming
SQL analytics, Python/Spark and ML on a single platform
Warehouse migrations from Oracle, Teradata and SQL Server
Scalable, cost-effective architecture
Real-time data pipelines and streaming
Kafka · Spark Streaming · Flink · CDCReal-time pipelines on Kafka, Spark Streaming, Flink or cloud-native streaming services: change data capture, stream processing, real-time aggregations and streaming analytics that turn batch reporting into continuous insight.
When it fitsAnalytics takes days or weeks because pipelines are batch-oriented, unreliable or need manual work.
Trade-offsStreaming is scoped one event stream at a time. Batch pipelines stay where daily data is enough.
How the plan covers it
Queue this scope →Change data capture from transactional systems
Stream processing and real-time aggregations
Real-time dashboards and operational analytics
Instant alerting on business-critical metrics
ETL/ELT pipeline development and orchestration
dbt · Airflow · Prefect · Databricks WorkflowsScalable ETL/ELT pipelines that extract data from many sources, transform it for analytics and load it into target platforms, with incremental processing, dependency management, error handling and monitoring.
When it fitsYour reporting runs on spreadsheets and manual exports, and data does not move reliably from transactional systems to analytics.
Trade-offsEach new source adds integration work. Sources go on the same backlog in priority order.
How the plan covers it
Queue this scope →Databases, APIs, SaaS tools, event streams and files integrated
Incremental processing and dependency management
Pipeline CI/CD with tests on every change
Failures alerted immediately with automated diagnostics
Data governance and data quality
Catalog · lineage · GDPR · CCPA · SOC 2Data catalogs, lineage tracking, quality monitoring, access controls and compliance frameworks for GDPR, CCPA and SOC 2. Automated quality checks, anomaly detection, schema validation and data observability keep the data behind analytics and ML trusted.
When it fitsStakeholders question the numbers and reports disagree.
Trade-offsCatalogs, lineage and access controls add work up front. They are what make governed self-service safe.
How the plan covers it
Queue this scope →Data catalogs, lineage tracking and access controls
Automated quality checks and schema validation
AI anomaly detection and data observability
Compliance frameworks for regulated data
ML/AI infrastructure and MLOps
MLflow · SageMaker · Databricks MLML-ready data infrastructure: feature stores, model training pipelines, experiment tracking, model serving and monitoring. MLOps on MLflow, SageMaker or Databricks ML lets data scientists train, deploy and monitor models in production.
When it fitsYour data scientists spend 80% of their time wrangling data instead of building models.
Trade-offsThis builds the data and ML infrastructure. LLM products, RAG systems, agents and fine-tuning are AI development work on the same plan.
How the plan covers it
Queue this scope →Feature stores and model training pipelines
Experiment tracking, model serving and monitoring
The data foundations RAG and fine-tuning depend on
Standardised infrastructure and automated workflows
Data analytics services: BI and self-service analytics
Tableau · Power BI · Looker · MetabaseAnalytics infrastructure and BI tools connected to governed data platforms: semantic layers, data marts and reusable metrics, with self-service analytics that let business users find answers without data team bottlenecks.
When it fitsEvery report, dashboard or data question waits on the data team.
Trade-offsSelf-service needs governed datasets and a semantic layer first. Analytics inside your own product is a separate scope, the Embedded Analytics Feature.
How the plan covers it
Queue this scope →Semantic layers, data marts and reusable metrics
BI tool deployment and configuration
Self-service analytics for business users
Organisation-wide, data-driven decision making
What's included in data engineering and analytics
Platform, pipelines, governance and analytics. Managed together.
Every Delivery Plan · Core comes with the full data team and the tooling around it: data strategy, platform and pipeline engineering, AI data-quality agents and analytics work, run by one delivery owner. For data analytics consulting, the team agrees the business questions first, then builds the data engineering that answers them.
Delivery managementOne accountable delivery owner.Milestones agreed before work begins.
01Data landscape assessmentSources inventoried, use cases gathered, quality and governance maturity evaluated, and a roadmap with success metrics
02Lakehouse and warehouse platformsDatabricks, Snowflake and cloud-native data services on AWS, Azure and GCP
03ETL/ELT pipelinesdbt and Spark transformations, orchestrated with Airflow, Prefect and Databricks Workflows
04Real-time streamingKafka, Spark Streaming and Flink, with change data capture from your databases
05Data governance and qualityCatalogs, lineage, access controls, automated quality checks and AI anomaly detection
06DataOpsVersion-controlled transformations, automated tests and CI/CD for data workflows
07BI and self-service analyticsTableau, Power BI, Looker and Metabase on a governed semantic layer
08ML infrastructure and MLOpsFeature stores, training pipelines and model serving on MLflow, SageMaker and Databricks ML
10Platform and code ownershipAll pipelines, models, dashboards and data are yours from day one.
100%A live pipeline is not the finish line.
Pipeline expansionMore sources added and pipelines extended as your needs grow.
Managed data operationsPipeline monitoring, incident response and maintenance.
Cost and performanceCloud costs per query and storage, and pipeline performance, kept under review.
Knowledge transferYour engineers trained hands-on, with a handoff for full platform ownership when you want it.
Runs on the Scrums.com platform
Every Delivery Plan runs on the Enterprise AI Platform for software engineering.
Your team, its AI agents and your delivery data sit on one governed platform. That is what powers your delivery board, the data-quality and code-review agents, and the talent bench behind your plan.
Your planDelivery Plan · CoreData, AI, backend, web, mobile and QA scopes on one backlogSee pricing →TalentAI engineers and teamsCertified engineers, CTOs and dedicated teamsHire engineers →AI Catalog163 agents · 101 MCPs · 30 modelsDeployable AI for engineering orgsBrowse the catalog →PlatformEnterprise AI PlatformAgent harness, AI and MCP gateways, governanceExplore the platform →
What your data team draws on, already part of your planBrowse the full AI catalog →
Engineering talent
Data engineers and teams
Data engineers, data architects and ML engineers from the Scrums.com bench, matched to your platform and domain.
AI Catalog
Agents, MCPs and models for data
The data-quality, code-review and release agents that run alongside your team, plus the MCP servers they use, such as Databricks SQL MCP Server and Google BigQuery MCP Server.
AI infrastructure
Infrastructure for data platforms and ML
Data, compute and AI infrastructure, deployable from the catalog with your $500 first-month credits.
Compare
One data hire covers one role. The plan covers the whole team.
A data platform needs architecture, pipelines, governance, analytics and ML work. Hiring covers them one role at a time. The Delivery Plan covers them in one monthly price.
Compare
RecommendedScrums.com Delivery Plan · Core
In-house data engineer
Traditional data consultancy
Roles covered
Platform, pipelines, governance, BI, MLOps
One role per hire
Depends on the consultancy bench
Time to start
48 hours to first code in production
3 to 6 months of recruitment first
Proposal and contract cycle first
How you pay
Salary, training, tool licensing and recruiting fees
Project quotes and change requests
Who runs delivery
One accountable delivery owner
You manage the work
The consultancy project manager
Visibility
Every pipeline run, quality score and dashboard release on the Scrums.com Platform
Your own tooling
Status reports by email
40-60%†lower cost than US and UK hires
Start for Basis: the figure that the current Scrums.com data engineering page states against US and UK hires. It is not a quote for your scope.
What you get
Why companies choose managed data analytics services
01
One lakehouse, not three systems
SQL analytics, Python/Spark and ML on one platform, with no duplication across warehouses and lakes.
02
One monthly price
One plan price for platform, pipelines and analytics. Your scope sets the number of months.
03
DataOps from the first pipeline
Version-controlled transformations, automated tests and CI/CD. Every pipeline change is reviewed, tested in staging and deployed automatically.
04
Human plus AI
Agents watch pipelines for anomalies, schema drift and quality issues. Mean time to detection drops from hours to minutes.
05
Data people trust
Automated quality checks, lineage and observability: 80% fewer data incidents.
06
ML-ready from day one
Feature stores, training pipelines and model serving are built in, not added afterwards.
Customers
Data engineering and analytics case studies
Teams are matched to your domain and your stack: Databricks, Snowflake, Kafka, dbt, Airflow and your BI tools.
Featured integration case study
NTT: decoupling a core platform from its ERP
- Challenge
- NTT’s ISPIC platform runs customer management, order processing and workflows. It was tightly coupled to Oracle ERP while NTT moved to SAP, and both ERPs had to run in parallel.
- Platforms and technologies
- A PHP backend, Apache Camel routing, STOMP messaging, Oracle ERP (legacy) and SAP (target), tracked in ServiceNow SPM.
- Work delivered
- Workflows separated from ERP-specific rules, SAP payload capture in ISPIC, standard payload publishing over STOMP and Camel routes for parallel Oracle and SAP paths, in 3-week sprints.
- Result
- ISPIC no longer depends directly on one ERP. It supports Oracle and SAP at the same time, routes SAP payloads through standard messaging and kept operations running through the transition.
Scopes
Data engineering and analytics scopes you can queue
One Delivery Plan · Core covers your development priorities across services. Start with data, then queue mobile, web, backend, AI or QA scopes on the same backlog, or describe your own.
DEData engineering17 scopesMMobile appsExplore ↗WWeb appsExplore ↗AIAI developmentExplore ↗APIBackend & APIsExplore ↗QAQA & testingExplore ↗OPSDevOps & reliabilityExplore ↗→All scopesCatalog
SprintSC-01
Analytics Performance Optimization
Speed up slow dashboards, queries, models, and refresh pipelines without changing the business definitions people rely on.
SprintSC-02
Cloud Data Warehouse Implementation
Create a structured cloud warehouse with a tested transformation layer and modeled datasets ready for analytics and BI. Finish state: governed, documented datasets your analysts actually query.
SprintSC-03
Customer 360 Data Model
Unify customer identities and interactions from multiple systems into a coherent customer-level data model. Finish state: one queryable customer record per real customer, with identity resolution you can audit.
SprintSC-04
Customer Cohort & Retention Dashboard
Build cohort-level retention, churn, expansion, activation, and lifecycle analysis on your product and billing data.
PodSC-05
Data & Analytics Pod
Ongoing data engineering and analytics capacity working a metrics backlog: pipeline build and operations, self-service BI, AI-powered data quality, and ML-ready infrastructure — delivered as a standing pod.
SprintSC-06
Data Lake Implementation
Centralize structured and unstructured data in a governed lake for analytics, ML, and downstream processing. Finish state: a governed lake teams can actually find things in — not a data swamp.
SprintSC-07
Data Pipeline Foundation
A working ELT baseline for your business: source ingestion, a governed warehouse, and your first dashboards — built on dbt, Airflow, and your cloud, with data quality checks from the first pipeline.
SprintSC-08
Data Quality Monitoring System
Implement automated checks, thresholds, alerts, lineage signals, and issue visibility for critical datasets. Finish state: data problems found by monitors in hours, not by executives in meetings.
SprintSC-09
Embedded Analytics Feature
Add customer-facing charts, metrics, filters, or reports inside an existing software product, with tenant-safe data access and production performance.
SprintSC-10
Engineering Analytics Dashboard
Create delivery, throughput, reliability, quality, and developer-workflow visibility from your engineering systems, without manual reporting overhead.
SprintSC-11
Executive KPI Dashboard
Deliver a decision-ready dashboard for a defined executive scorecard, with agreed metrics, documented source logic, and automated refresh.
SprintSC-12
Forecasting & Predictive Analytics Pilot
Build and validate one predictive model tied to a defined operational or commercial decision, with an honest read on whether it beats the baseline.
The plan in full
Data engineering and analytics services pricing
Core costs Was $8,000£6,700R140,000 now $4,500£3,750R79,000/month for one active workstream. Your platform’s scope determines the delivery schedule and total cost. You can use the same subscription for other development priorities.
Best for companies whose data is siloed across systems, whose analytics take days or weeks, or whose stakeholders do not trust the numbers.
Delivery Plan · Core
One plan for your software backlog.
Now $4,500£3,750R79,000/ monthWas $8,000£6,700R140,000
One active workstream. Managed delivery.
Start your Delivery Plan →
Billed monthly · excl. VAT/sales tax · onboarding starts immediately after checkout
Included every month
Delivery owner. One accountable owner runs your data engineers, analytics engineers and ML engineers.
One backlog across your development priorities. You set the order; requests are scoped and delivered one workstream at a time.
Visibility. Milestones agreed up front. Pipeline runs, data quality and releases live on the Scrums.com Platform.
Ownership. Pipelines, models, dashboards and data are yours from day one, with handover included.
Work is scoped and delivered within your plan’s capacity. Priorities can change; additional parallel workstreams require more capacity.
First-month benefits
14DAYS
First-delivery guaranteeYour first agreed deliverable ships within 14 days of scope and access being confirmed, or your first month is free.
$500 in AI infrastructure catalog creditsMONTH 1
Annual-plan benefits$10,000 in specialist services
Technical Assessment & Delivery RoadmapWe review your systems, rank your priorities and agree the first deliverable.
CI/CD & Quality Pipeline SetupYour pipelines, environments and quality checks, set up and handed over to you.
You buy monthly delivery capacity, not a fixed project price. Infrastructure usage and third-party services are billed separately. Full Delivery Plan coverage and terms →
Need more parallel workstreams? Talk to us →
Day 0Scope and access confirmedOnboarding starts at checkout. You confirm scope and share data sources, cloud accounts and repos.
Within 48 hoursFirst code in productionFirst change merged and deployed through your pipeline.
Within 14 daysFirst agreed deliverable shipsFor example the first priority source ingested into your platform, with quality checks running.
If it does not shipYour first month is freeYou do not pay for month one.
The first deliverable is agreed in writing before the clock starts. The clock starts when scope and access are confirmed. Applies to the first deliverable on a new plan.Read the full Delivery Plan terms →
FAQ
Data engineering and analytics questions, answered
Still deciding? Talk to sales and get a scoped answer for your data platform.
Delivery Plan · Core costs Was $8,000£6,700R140,000 now $4,500£3,750R79,000 a month, excluding VAT or sales tax. That buys one active workstream with a managed data team. It is monthly delivery capacity, not a fixed price for a complete data platform, so your scope sets the number of months.
Yes. Delivery Plan · Core covers mobile, web, backend, AI, QA and DevOps work as well as data engineering and analytics. All of it sits in one prioritised backlog and is delivered within your plan’s capacity, one active workstream at a time. Priorities can change; additional parallel workstreams require more capacity.
Data landscape assessment and strategy, lakehouse and warehouse platforms, ETL/ELT and streaming pipelines, data governance and quality, DataOps, BI and self-service analytics, ML infrastructure and MLOps, and one accountable delivery owner. Every plan includes $500 in AI infrastructure catalog credits in your first month. Annual plans also include $10,000 in specialist services: a technical assessment, a delivery roadmap and CI/CD pipeline setup.
You rank the backlog. Your delivery owner scopes the top request, agrees acceptance criteria with you, and the team delivers it. You can reorder anything that has not started. One request is active at a time on Core; additional parallel workstreams require more capacity.
The clock starts when you confirm scope and give access to your data sources, cloud accounts and repositories. Within 48 hours the team has its first code in production in your environment. Within 14 days your first agreed deliverable ships, for example the first priority source ingested into your platform with quality checks running. If it does not, your first month is free.
48 hours to first code in production means the first change merged and deployed through your pipeline, counted from the day scope and access are confirmed. 14 days means the deliverable named at kickoff ships within 14 days of that date, or your first month is free. “Thousands of companies” counts organisations that have bought engineering services from Scrums.com since 2012. 80% fewer data incidents, 4 to 6 weeks to the first analytics use case and 1 to 2 months to a basic platform are the figures the current Scrums.com data engineering and analytics page states. 40-60% lower cost is the figure that page states against US and UK hires; it is not a quote for your scope.
A basic platform (one data warehouse, batch pipelines and basic reporting) takes 1 to 2 months. A standard lakehouse (real-time pipelines, BI tools and governance) takes 3 to 4 months. An enterprise platform (comprehensive pipelines, ML infrastructure and data mesh) takes 6 to 12 months. Delivery is incremental: the first analytics use cases go live within 4 to 6 weeks while the platform grows.
Yes. Traditional warehouses (Oracle, Teradata, SQL Server) migrate to modern lakehouses (Databricks, Snowflake). The options are a parallel run, where the lakehouse is built alongside the warehouse and workloads move gradually, a phased migration of pipelines and reports, or a hybrid. Most migrations complete in 6 to 12 months with zero downtime for critical analytics.
That is a good starting point. Automated pipelines replace manual exports, spreadsheet data moves into a governed platform, and business users get BI tools instead of spreadsheets. Migration starts with the critical reports. Within 2 to 3 months, most critical spreadsheet-based analytics run on an automated, reliable data platform.
With a data quality framework: schema validation, automated checks for completeness, accuracy and uniqueness, AI anomaly detection, lineage from source to analytics, pipeline monitoring that alerts on failures and slowdowns, and data observability. This cuts data quality incidents by 80% and mean time to detection from hours to minutes.
Yes: databases (PostgreSQL, MySQL, SQL Server, Oracle, MongoDB), SaaS applications (Salesforce, HubSpot, Stripe, Google Analytics), event streams (Kafka, Kinesis, Pub/Sub), APIs and webhooks, file systems (S3, Azure Blob, SFTP), legacy systems and custom applications.
Hiring in-house takes 3 to 6 months of recruitment at $150K to $250K a year per engineer, plus training, tool licensing and the risk of losing expertise to turnover. The plan gives you a managed team with platform expertise across pipelines, ML and governance, at 40-60% less than US and UK hires. The team also trains your engineers hands-on, so you can build in-house capability over time.
On the Scrums.com Platform: time to insight, data quality (completeness, accuracy and freshness), pipeline reliability (uptime, failure rate, MTTR), self-service adoption, ML training time, cloud costs per query and storage, and internal user satisfaction. Quarterly reviews show the return through faster analytics, less data team toil and faster ML.
You choose: ongoing platform engineering that adds sources and optimises performance, managed data operations with monitoring, incident response and maintenance, advisory from a part-time data architect, or knowledge transfer and a handoff for full platform ownership. Most clients choose managed operations or advisory.
DATA ENGINEERING & ANALYTICS · SORTED
Data your teams trust, ready for analytics and AI.
Delivery Plan · Core: one monthly price, one backlog across your development priorities, one active workstream and a managed team for data platforms, pipelines, governance and analytics.
PLATFORMS · PIPELINES · GOVERNANCE · ANALYTICS · MLOPS