Data Architecture & Platform Design

Data architecture is the design and structure that determines how your organization collects, stores, processes, and accesses data. It includes three main components: (1) data models defining tables, schemas, relationships, and how data is organized, (2) data platforms and infrastructure including databases, data warehouses, data lakes, and cloud services, and (3) data pipelines and workflows that move and transform data between systems. Modern data architecture focuses on cloud-native platforms (Snowflake, BigQuery, Redshift), scalable designs handling gigabytes to petabytes, and supporting multiple use cases (analytics, ML, operational reporting, embedded analytics). Good data architecture consolidates fragmented data sources into a unified platform, enables fast queries on large datasets, supports governance and security requirements, and scales cost-effectively as data volumes and users grow. Bad data architecture results in slow queries, data silos, high costs, and inability to support analytics and ML initiatives.

Hire a data architect when you're: (1) spending 20+ hours per week manually extracting and combining data from multiple disconnected systems, (2) experiencing slow query performance—dashboards that take 30+ seconds to load or analytical queries that time out, (3) struggling with fragmented data silos where different teams report different numbers for the same metrics, (4) planning analytics, ML, or data science initiatives but lacking the infrastructure to support them, (5) migrating from legacy on-premise systems (Oracle, Teradata) to modern cloud platforms, or (6) scaling beyond 1TB of data and experiencing performance degradation or cost escalation. The ideal time is before data problems become critical bottlenecks—when data volumes reach 100GB-1TB, when you have 5-10 data sources that need consolidation, or when you're hiring your first data team members and need proper infrastructure for them to work with. Most companies reach this point at 50-200 employees or Series A/B funding stage.

Data architecture consulting typically costs $25,000-$50,000 for startups and small businesses needing foundational data platform setup with 3-10 data sources and basic pipelines, $50,000-$125,000 for growing companies requiring comprehensive architecture with 10-25 sources, advanced modeling, governance, and migration from legacy systems, or $125,000-$300,000+ for enterprises with petabyte-scale data, complex compliance requirements, multi-region deployments, or data mesh implementations. Pricing varies based on data volume and complexity (gigabytes vs petabytes), number of data sources and pipelines, architectural patterns (simple data warehouse vs data mesh or real-time streaming), migration complexity from legacy systems, and governance and compliance requirements (GDPR, HIPAA, SOC 2). Hourly rates for data architects range from $75-$350 depending on experience, but project-based pricing delivers better value. Most clients achieve 4-6x ROI within 6-12 months through time savings (20-40 hours/week), cost optimization (30-60% infrastructure savings), and better decisions enabled by accessible, reliable data.

Typical deliverables include: (1) Architecture Blueprint with platform recommendations, detailed diagrams, data flows, cost projections, and implementation roadmap, (2) Production Data Platform with configured Snowflake, BigQuery, or Redshift infrastructure and security policies, (3) Data Models with implemented fact and dimension tables, partitioning, clustering, and optimization, (4) ETL/ELT Pipelines with automated data extraction from all sources, dbt transformations, and orchestration (Airflow/Prefect), (5) Data Governance Framework with role-based access control, audit logging, data lineage tracking, and compliance policies (GDPR, HIPAA, SOC 2 if needed), (6) Performance Optimizations including query tuning, materialized views, caching strategies, and cost management, (7) Documentation including architecture docs, data dictionaries, pipeline specifications, runbooks, and best practices, and (8) Training materials for data engineers, analysts, and administrators with 30-90 days post-launch support. You own all infrastructure, code, models, and documentation—no ongoing licensing requirements from StepInsight, though cloud platforms have their own costs.

Data architecture implementation typically takes 8-20 weeks depending on scope and complexity. Small projects (3-10 data sources, basic data warehouse, single team) take 8-12 weeks covering platform setup, data modeling, pipeline development, and training. Medium projects (10-25 sources, advanced analytics support, governance framework, ML infrastructure) take 12-18 weeks including comprehensive modeling, orchestration, optimization, and testing. Large enterprise projects (25+ sources, petabyte-scale data, legacy migration, data mesh, multi-region) take 18-24+ weeks with extensive planning, phased rollout, parallel running, and change management. Timeline depends on data source complexity and readiness, migration requirements from legacy systems, team availability for requirements and testing, governance and compliance requirements, and whether you need real-time streaming or advanced ML infrastructure. Most clients see value within 4-6 weeks when initial pipelines and dashboards go live, with full platform maturity by end of engagement. ROI typically achieved within 3-6 months post-launch.

StepInsight differentiates through: (1) Hands-On Implementation—we don't just create architecture diagrams and recommendations; we build production-ready systems with code, pipelines, and infrastructure, not 100-page strategy documents, (2) Modern Stack Expertise—we're experts in modern cloud platforms (Snowflake, BigQuery, Redshift), modern data tools (dbt, Fivetran, Airflow), and modern patterns (data lakehouse, mesh, fabric), not legacy enterprise systems, (3) Full-Stack Capabilities—our team combines data engineering with software development, enabling end-to-end solutions from infrastructure to embedded analytics, (4) Cost-Conscious Design—we optimize for both performance and cost, implementing strategies that reduce cloud bills 30-60% while improving performance, and (5) Startup to Enterprise Experience—we've built data platforms for Series A startups ($1k/month budgets) and public enterprises (petabyte-scale), understanding how to design for current needs while scaling for growth. We're not a big consulting firm with junior consultants—you work directly with senior data architects and engineers who've implemented 200+ data platforms.

Snowflake is typically best for: organizations wanting platform flexibility (runs on AWS, Azure, GCP), companies prioritizing ease of use and instant scaling, teams needing separate compute clusters for different workloads, and budgets allowing premium pricing ($23-$40/TB/month for compute). BigQuery is typically best for: organizations already using Google Cloud Platform (GCP), teams wanting serverless, zero-maintenance infrastructure, pay-per-query pricing models ($5/TB queried, $20-$23/TB storage), and built-in ML capabilities (BigQuery ML). Redshift is typically best for: AWS-centric organizations with existing AWS infrastructure, cost-conscious teams with steady query workloads (predictable hourly pricing), and companies prioritizing tight AWS service integration (S3, Lambda, EMR). Honestly, all three are excellent modern platforms—the choice depends on your existing cloud ecosystem, budget and pricing model preferences (pay-per-query vs. hourly compute), team expertise and learning curve tolerance, and specific performance requirements. We're experts in all three platforms and recommend the right tool based on your specific needs and constraints, not vendor partnerships. Many organizations use multiple platforms for different use cases.

A data lakehouse combines the flexibility and cost-efficiency of data lakes (storing raw, unstructured data) with the structure and ACID transactions of data warehouses (organized, queryable data). Built on open formats like Delta Lake, Iceberg, or Hudi, lakehouses store data in object storage (S3, GCS, ADLS) while providing warehouse-like features including SQL queries, ACID transactions, schema enforcement, time travel, and unified batch and streaming. Use a data lakehouse when: (1) you need to store diverse data types (structured, semi-structured, unstructured) including JSON, logs, images, videos alongside tabular data, (2) you want to support both analytics (SQL queries, BI dashboards) and ML/AI (training data, feature engineering) on the same platform, (3) you need cost-effective storage for large volumes of data (petabytes) with infrequent access patterns, (4) you require flexibility to change schemas over time without expensive migrations, or (5) you want to avoid data duplication and ETL overhead of maintaining separate data lake and data warehouse. Use a traditional data warehouse (Snowflake, BigQuery, Redshift) if your data is primarily structured, your main use case is SQL-based analytics and BI, and you prefer managed, zero-maintenance platforms. Platforms: Databricks (Delta Lake), AWS (Apache Iceberg on S3), Dremio (Apache Iceberg).

Data mesh is an organizational and architectural approach that decentralizes data ownership, treating data as a product owned by domain teams (sales, marketing, product, finance) rather than a centralized data team. Each domain manages its own analytical data, exposing it to other domains through well-defined interfaces with data-as-a-product principles. Consider data mesh if: (1) you're a large organization (500+ employees) with multiple business domains and data teams, (2) centralized data teams create bottlenecks—4-8 week backlogs for new data requests, (3) business domains have unique data needs and move faster independently than through central teams, (4) you have domain expertise and data literacy across business units, and (5) you can invest in federated governance ensuring consistency and quality across domains. DON'T implement data mesh if: you're a small organization (under 200 employees) where centralized data teams are efficient, you lack data engineering expertise in domain teams, or you don't have mature data governance and observability practices. Data mesh is organizational transformation, not just technology—it requires cultural change, clear ownership models, and sophisticated tooling. Most organizations benefit from centralized data platforms first, evolving toward data mesh patterns as they scale and mature. We help assess whether data mesh fits your stage and implement federated architectures if appropriate.

Yes, we specialize in migrating data platforms from legacy on-premise systems (Oracle, SQL Server, Teradata, DB2, Netezza, custom data warehouses) to modern cloud platforms (Snowflake, BigQuery, Redshift). Our migration approach includes: (1) assessment and planning documenting current architecture, data volumes, dependencies, risks, and rollback strategies, (2) platform selection and architecture design optimizing for cloud-native performance and cost-efficiency, (3) schema conversion and optimization redesigning data models for modern platforms with improved partitioning and performance, (4) data migration with validation ensuring 100% accuracy through automated reconciliation and testing, (5) ETL/ELT pipeline replatforming rebuilding workflows on modern tools (Fivetran, dbt, Airflow) replacing legacy ETL tools (Informatica, DataStage, SSIS), (6) parallel running both old and new systems during transition enabling thorough validation and safe rollback, and (7) phased cutover minimizing disruption to business operations. Typical migration timelines: 12-20 weeks for medium complexity, 20-30+ weeks for large, complex enterprises. Clients typically achieve 40-60% cost reduction, 5-10x performance improvement, and elimination of maintenance burden after migration.

We implement comprehensive data quality and governance frameworks including: (1) Data Quality Checks—validation rules at ingestion, transformation, and consumption layers checking completeness, accuracy, consistency, and timeliness with automated alerts on failures, (2) Data Lineage Tracking—complete visibility into data origins, transformations, and dependencies using tools like dbt documentation, data catalogs (Atlan, Alation), and lineage platforms (Monte Carlo, Datafold), (3) Access Controls—role-based access control (RBAC) with granular permissions at database, schema, table, column, and row levels ensuring users only access authorized data, (4) Audit Logging—complete trails of all data access, modifications, and administrative actions for security and compliance, (5) Data Catalog—searchable inventory of all datasets with metadata, documentation, ownership, and quality metrics, (6) Data Quality Monitoring—continuous monitoring with anomaly detection, drift detection, and automated testing using tools like Great Expectations or dbt tests, and (7) Compliance Frameworks—implementation of GDPR, HIPAA, SOC 2, CCPA, or industry-specific requirements with data classification, retention policies, and encryption. We establish governance processes including data stewardship, ownership models, and change management ensuring sustainability beyond initial implementation.

ETL (Extract-Transform-Load) extracts data from sources, transforms it on a separate processing server, then loads clean data into the warehouse. ELT (Extract-Load-Transform) extracts data from sources, loads raw data into the warehouse first, then transforms it using the warehouse's compute power. Modern cloud data warehouses (Snowflake, BigQuery, Redshift) have powerful, scalable compute enabling ELT as the preferred approach because: (1) cloud warehouses handle transformations faster and more cost-effectively than separate ETL servers, (2) raw data is preserved in the warehouse enabling reprocessing if transformation logic changes, (3) transformations can be version-controlled, tested, and documented using tools like dbt, (4) schema changes in sources don't break pipelines—raw data is loaded first, transformations adjusted later, and (5) different teams can create different views of the same raw data for their needs. Use ETL when: (1) you need to cleanse or mask sensitive data before it enters the warehouse for compliance reasons, (2) source systems can't handle extraction load and require transformation to reduce data volume, or (3) you're working with legacy systems or on-premise warehouses without modern compute. We implement ELT by default using modern tools (Fivetran/Airbyte for extraction and loading, dbt for transformations) but adapt based on your specific constraints and requirements.

We implement comprehensive cost optimization strategies including: (1) Compute Management—auto-scaling compute resources based on workload, suspending warehouses during idle periods, and right-sizing clusters to match actual usage patterns reducing costs 30-50%, (2) Storage Optimization—lifecycle policies moving cold data to cheaper storage tiers, compression algorithms reducing storage footprint by 50-80%, and archival strategies for historical data no longer actively queried, (3) Query Optimization—tuning expensive queries, implementing partitioning and clustering to reduce data scanned, and creating materialized views for common aggregations reducing query costs 50-80%, (4) Resource Monitoring—dashboards tracking costs by team, user, query, and use case identifying top spenders and optimization opportunities, (5) Budget Alerts—proactive notifications when costs exceed thresholds preventing surprise bills, (6) Incremental Processing—loading only changed data rather than full refreshes reducing compute and time, and (7) Tiered Storage—using appropriate storage tiers (hot, warm, cold, archive) based on access patterns. We establish cost visibility from day one enabling data-driven optimization decisions. Typical results: 30-60% cost reduction compared to unoptimized cloud platforms while maintaining or improving performance. We also provide monthly/quarterly cost reviews and optimization recommendations.

We've delivered data architectures across 18 industries including: SaaS and software companies (product analytics, user behavior, subscription metrics), healthcare organizations (patient data platforms, clinical analytics, HIPAA-compliant architectures), financial services (transaction processing, risk analytics, fraud detection, SOC 2 compliance), e-commerce and retail (inventory management, customer 360, supply chain analytics), real estate (property data platforms, portfolio analytics, market intelligence), logistics and transportation (fleet management, route optimization, delivery tracking), nonprofits and membership organizations (donor analytics, program impact measurement, member engagement), construction and field services (project data platforms, equipment tracking, job costing), education (student data platforms, learning analytics, enrollment management), and professional services (project profitability, resource utilization, client analytics). While industry context helps—we understand HIPAA for healthcare, SOC 2 for SaaS, GDPR for European operations—most data architecture challenges are universal: consolidating fragmented data, designing scalable models, building reliable pipelines, and implementing governance. We bring 10+ years of best practices and adapt them to your specific industry needs, compliance requirements, and business context.

After launch, you receive 30-90 days of post-launch support (depending on engagement tier) covering: questions from your team as they use the platform, minor adjustments to data models or pipelines based on real-world usage, performance optimization as data volumes and users grow, troubleshooting issues and pipeline failures, and monitoring data quality and costs. We provide comprehensive training and documentation enabling your team to independently manage the platform—adding new data sources, modifying transformations, managing users and access controls, and troubleshooting common issues. For ongoing needs, we offer optional retainer arrangements for: new pipeline development as you add data sources or use cases, performance tuning and cost optimization as you scale, advanced features like ML infrastructure, real-time streaming, or embedded analytics, data governance and compliance support, and strategic consultation on architecture evolution. Many clients are fully independent after launch; others engage us for ongoing enhancements as their data needs grow. You own all infrastructure, code, data models, and documentation—no lock-in. We also provide recommendations for hiring internal data engineers or architects if you want to build in-house capabilities, including job descriptions, interview questions, and onboarding plans.