Data Engineering & Analytics Services

Our structured data platform implementation delivers value incrementally while building toward comprehensive, enterprise-grade data capabilities.

Data Landscape Assessment & Strategy

We begin by understanding your current data landscape, identifying pain points, mapping data sources, and designing target data architecture. Our team assesses existing infrastructure, interviews stakeholders, evaluates data quality, and creates a prioritized roadmap aligned with business objectives.

Key Activities:

• Current data infrastructure and tooling assessment
• Data source inventory and integration complexity evaluation
• Stakeholder interviews identifying analytics and ML use cases
• Data quality and governance maturity assessment
• Performance and scalability bottleneck identification
• Technology stack evaluation (Databricks, Snowflake, cloud services)
• Target architecture design (lakehouse, data mesh, modern stack)
• Use case prioritization and phased roadmap creation
• Success metrics and KPI definition
• Cost-benefit analysis and ROI projections

Deliverable: Data assessment report, target architecture diagrams, implementation roadmap, technology recommendations

Platform Foundation & Initial Pipelines

Establish data platform foundations including lakehouse infrastructure, orchestration tools, governance frameworks, and initial high-priority data pipelines. We set up environments, implement core infrastructure, onboard priority data sources, and deliver first analytics use cases.

Key Activities:

• Data platform provisioning (Databricks, Snowflake, cloud data services)
• Storage layer setup (data lake, object storage configuration)
• Orchestration platform deployment (Airflow, Databricks Workflows, Prefect)
• Data governance tooling setup (data catalog, lineage, quality monitoring)
• Initial data pipeline development for priority sources
• Data transformation framework implementation (dbt, Spark)
• Quality validation and testing framework establishment
• Monitoring and alerting configuration
• Team access provisioning and training kickoff
• First analytics use case delivery (dashboards, reports)

Deliverable: Operational data platform, initial pipelines ingesting priority data, first analytics dashboards, monitoring systems

Pipeline Expansion & Analytics Enablement

Progressively expand data coverage, implement additional pipelines, build analytics capabilities, and establish self-service patterns. We onboard remaining data sources, create data marts, deploy BI tools, and enable business users for self-service analytics.

Key Activities:

• Comprehensive data source integration (databases, APIs, SaaS, events)
• Real-time streaming pipeline implementation (Kafka, Spark Streaming)
• Data transformation development (cleaning, aggregation, enrichment)
• Historical data migration and backfilling
• Semantic layer and metrics framework creation
• BI tool deployment and configuration (Tableau, Power BI, Looker)
• Self-service analytics capabilities establishment
• Data quality monitoring and validation expansion
• Performance optimization and cost management
• Team training on data platform and tools
• Documentation and runbook creation

Deliverable: Comprehensive data coverage, analytics-ready data marts, deployed BI tools, self-service capabilities, trained users

ML Infrastructure & Advanced Capabilities

Build ML/AI infrastructure, implement advanced analytics, optimize platform performance, and establish DataOps maturity. We deploy MLOps platforms, create feature stores, implement data mesh patterns for large organizations, and continuously optimize cost and performance.

Key Activities:

• ML infrastructure setup (feature stores, training pipelines, model serving)
• MLOps platform deployment (MLflow, SageMaker, Databricks ML)
• Advanced analytics implementation (predictive, prescriptive, real-time)
• Data mesh implementation for decentralized data ownership (if applicable)
• Data product development and self-service platform capabilities
• Performance tuning and query optimization
• Cost optimization (storage tiering, compute rightsizing, caching)
• Advanced governance (sensitive data discovery, access policies, compliance automation)
• Disaster recovery and business continuity implementation
• Continuous improvement and platform evolution

Deliverable: ML-ready infrastructure, advanced analytics capabilities, optimized platform performance, mature DataOps practices

What Impacts Data Engineering & Analytics Costs?

Data Volume & Velocity
Processing gigabytes daily costs less than terabytes or petabytes. Real-time streaming pipelines processing millions of events per second require more infrastructure and engineering than batch pipelines running nightly. Higher volume and velocity = more compute, storage, and engineering complexity.

Data Source Diversity & Complexity
Integrating 5 standard databases costs less than connecting 50+ diverse sources (APIs, SaaS tools, legacy systems, event streams, files). More sources = more connectors, transformations, quality checks, and maintenance overhead.

Analytics & ML Requirements
Basic reporting dashboards cost less than advanced analytics (real-time, predictive, prescriptive) or production ML infrastructure (feature stores, training pipelines, model serving). More sophisticated analytics = more infrastructure and specialized data science engineering.

Data Governance & Compliance Needs
Basic data platforms cost less than comprehensive governance including data catalogs, lineage tracking, access policies, sensitive data discovery, and compliance automation (GDPR, HIPAA, SOC 2). Regulated industries require more governance engineering and tooling.

Organization Size & User Count
Supporting 10 analysts costs less than enterprise-wide self-service platforms serving hundreds of business users. More users = more semantic layers, data products, performance optimization, and support infrastructure.

Existing Infrastructure Maturity
Building data platforms from scratch requires more investment than modernizing existing warehouses or lakes with better tools and practices. Lower starting maturity = more foundational work.

Industry Benchmarks: What Data Engineering & Analytics Typically Costs

These are general industry ranges to help you budget:

Basic Data Platform (Small Scale, Limited Use Cases)
Single data warehouse, batch pipelines, basic reporting for small team
Industry range: $15K - $40K/month

Standard Data Platform (Mid-Sized, Growing Analytics)
Lakehouse architecture, real-time pipelines, BI tools, growing self-service capabilities
Industry range: $40K - $100K/month

Enterprise Data Platform (Large Scale, Comprehensive)
Multi-cloud lakehouse, extensive pipelines, ML infrastructure, data mesh, enterprise governance
Industry range: $100K - $300K+/month

Data Platform Transformation Projects
Initial platform build, migration, comprehensive pipeline development
Industry range: $200K - $800K per project

The Scrums.com Advantage: Modern Data Platforms at Predictable Costs

Unlike data consultancies focused on expensive enterprise tools, we deliver modern lakehouse-based platforms using cost-effective technologies (Databricks, open-source tools, cloud-native services) at 40-60% lower cost than traditional data warehousing approaches, while delivering superior performance, flexibility, and ML-readiness.

What Makes Our Data Engineering Different:

Lakehouse Architecture – Unified platform reducing costs 40-60% vs. separate warehouses and lakes while enabling analytics-to-ML workflows

DataOps Automation – Pipeline CI/CD, automated testing, and observability reducing data incidents by 80% and accelerating development

AI-Powered Data Quality – Automated anomaly detection, schema validation, and quality monitoring ensuring 99.9% data reliability

ML-Ready Infrastructure – Feature stores, training pipelines, and model serving built-in from day one, not afterthoughts

Self-Service Enablement – Semantic layers, data products, and governed self-service reducing data team bottlenecks

Cloud Cost Optimization – Storage tiering, compute rightsizing, and caching strategies reducing cloud data costs 30-50%

Proven Lakehouse Patterns – Pre-built architecture templates accelerating implementation 3-5x faster than custom builds

Three Ways to Structure Data Engineering Services

Dedicated Data Engineering Team
Full-time data engineers, data architects, and analytics engineers building and operating your data platform.
Best for: Organizations building comprehensive data platforms requiring sustained focus

Part-Time Data Specialists
Dedicated data engineers working part-time (20hrs/week) on pipelines, governance, and platform improvements.
Best for: Growing teams scaling data capabilities without full-time headcount

Augmented Data Engineers
Add individual data engineers, data architects, or ML engineers to your existing data team.
Best for: Teams with platform foundation needing specialized expertise (Databricks, streaming, MLOps)

Ready to See What Data Engineering Would Cost?

Pricing depends on data volume, source complexity, and analytics requirements. View our transparent pricing models or get a custom data engineering quote after a consultation.

View Our Pricing Models or Get Custom Data Engineering Quote