We design and implement data mesh architectures that decentralize data ownership to domain teams, eliminating centralized data team bottlenecks while ensuring federated governance, discoverability, and interoperability across the enterprise.
The centralized data team model breaks at scale. As organizations grow, a single platform team becomes the bottleneck for every analytics initiative across every business domain. Data requests queue up for months, context is lost in translation, and domain experts never get the timely data they need.
Data mesh inverts this model: domain teams own and publish their own data products, a self-serve platform removes technical barriers, and federated governance ensures enterprise-wide consistency without re-centralization. We have built and operated data mesh programs in some of the most data-intensive enterprises in the country.
Key differentiator: We design data mesh as an organizational operating model first, technology second, ensuring domain teams have the skills, incentives, and guardrails to sustain data product quality.
The four pillars of data mesh, and the specific tools and patterns we use to implement each one.
We work with your organization to define domain boundaries aligned to business capabilities, not org charts. Each domain team (e.g., Orders, Customers, Inventory) owns, governs, and serves their data products. We design incentive structures, team topologies, and SLA accountability frameworks that make domain ownership sustainable at scale.
We implement Zhamak Dehghani's data product architecture with discoverable, addressable, trustworthy, self-describing, and interoperable outputs. Data products are packaged as Iceberg table bundles with embedded schema, SLA metadata, quality scores, and lineage, published to the DataHub catalog with semantic search indexing and usage tracking.
The platform team provides golden-path infrastructure: Terraform modules for Iceberg table provisioning, dbt project templates, automated DataHub registration, and Airflow DAG scaffolding. A Backstage-powered data portal gives domain teams a single interface to register products, request access, monitor quality, and view cross-domain lineage graphs.
Governance policies are encoded as code and enforced automatically, not manually reviewed. AWS Lake Formation or Azure Purview enforces column-level access policies across all domain data products. dbt tests and Great Expectations quality rules are mandatory parts of every data product's CI/CD pipeline. Schema Registry enforces Avro/Protobuf contracts for streaming products.
DataHub as the central metadata plane with API-driven lineage scraping from dbt, Spark, Airflow, and Kafka. Confluent Schema Registry for streaming data contract enforcement. Monte Carlo for cross-domain data observability. Open Policy Agent (OPA) for attribute-based access control decisions across the mesh. Cube.dev as a cross-domain semantic layer aggregating metrics from multiple data products.
We implement the open data contract specification (ODCS), defining schema, SLA, data quality expectations, and owner contacts in machine-readable YAML. Contracts are version-controlled in Git and enforced in CI/CD: PRs that violate downstream consumer expectations are automatically rejected. Soda Core runs scheduled contract validation checks with Slack alerting on breach.
Data mesh is a multi-year organizational transformation. We sequence the journey to deliver early value, starting with the highest-demand domains and building platform capabilities incrementally rather than waiting for everything to be perfect before publishing the first data product.
Our teams embed with domain squads, build platform capabilities in parallel, and measure success with the metrics that matter: time from data request to production, data product quality scores, and analyst self-sufficiency rates.
Evaluate organizational maturity across five dimensions: data literacy, team autonomy, platform capabilities, governance readiness, and executive sponsorship. Identify two to three pilot domains where mesh will deliver the highest business impact. Produce a mesh implementation roadmap with 90-day, 6-month, and 12-month milestones and success metrics.
Embed with the pilot domain team (e.g., Orders or Customer) to build the first two to three production data products end-to-end. Establish the domain's data product repository, CI/CD pipeline, and DataHub registration. Document patterns as reusable templates for subsequent domains to follow. First production data product delivered within 90 days.
Build the platform capabilities that make domain ownership scalable: Terraform module library, dbt project scaffolding CLI, automated DataHub registration on merge, shared Airflow instance with domain-scoped DAG folders, and Backstage data portal for product discovery and access requests. Platform dogfoods on pilot domain feedback before broader rollout.
Define enterprise-wide standards: data product interface specification, mandatory quality expectations, PII classification requirements, and SLA tiers. Encode standards as CI/CD gates rather than review checklists. Deploy cross-domain observability with Monte Carlo and configure DataHub to surface cross-domain lineage automatically from pipeline metadata.
Onboard additional domains with platform team support. Establish a Data Mesh Center of Excellence (CoE) with domain champions, a data product maturity model, and internal community of practice. Track adoption with DataHub usage analytics. Publish a quarterly data mesh health scorecard to executive stakeholders covering product count, quality scores, and consumer satisfaction.
Enterprise data mesh transformations that eliminated bottlenecks and scaled data culture across organizations.
Helped a top-10 US retail bank transition from a centralized 40-person data engineering team to a federated mesh with 12 domain teams owning 85+ data products. The central team re-platformed as a platform team, reducing their support ticket load by 80%. DataHub catalogs 400+ datasets with automated lineage from Spark, dbt, and Airflow pipelines across all domains.
75% reduction in data request SLA violationsBuilt a data mesh for a global logistics company with domain teams in Shipment, Fleet, Customer, and Finance. Apache Iceberg on S3 with AWS Lake Formation enforces cross-domain access policies. Confluent Cloud Schema Registry enforces Avro contracts for streaming data products. 22 production data products deployed within the first 6 months using self-serve Terraform templates.
22 data products in production within 6 monthsDesigned a data mesh architecture for a federal health agency with strict data residency and HIPAA requirements. Azure Purview enforces column-level access across all domain data products. Data contracts encode HIPAA data categories with automated policy enforcement. Domain teams in Enrollment, Claims, and Provider now publish monthly data products with zero central team involvement.
100% domain self-serve, HIPAA audit-readyMigrated a rapidly growing e-commerce platform from a monolithic Redshift warehouse to a data mesh with domain teams for Products, Orders, Marketing, and Recommendations. Backstage data portal reduced data discovery time from 2 hours to under 5 minutes. Monte Carlo monitors 150+ cross-domain quality checks with automatic incident routing to domain owners.
4× faster data discovery, 90% fewer data incidentsStart with a Data Mesh Readiness Assessment: we evaluate your organizational maturity, identify pilot domains, and deliver a phased implementation roadmap in 2 weeks.