Skillset: Enterprise Data Architecture, Azure data bricks. Integrations · Multi-Layer Platform. Data layer designWe are building enterprise-grade data tools that sit on top of complex, existing customer environments and build a reusable data platform powering multiple products and client deployments. We need someone who has built data platforms before, not just pipelines, and can design a data layer that blends seamlessly into whatever stack our customer already runs.Job Description Design and own the data architecture layer that sits across all our enterprise productsMap and integrate into customer environments cloud warehouses, on-prem databases, legacy systems, third-party APIsBuild reusable data connectors, transformation pipelines, and a normalization layer that works regardless of the sourceDefine the canonical data models our products are built on — consistent, versioned, and documentedWork with product and AI teams to ensure data is structured correctly for downstream agent and analytics use casesOwn data quality — schema validation, lineage tracking, and monitoring across all pipelinesGuide technical decisions on storage, processing, and integration tooling as the platform scalesResponsibilities Data Architecture & ModelingDesigning multi-layer data architectures (raw → curated → serving)Canonical schema design, data contracts, and versioned data modelsData vault, dimensional modeling, or Lakehouse patterns depending on contextIntegration & Connectivity & OrchestrationDeep experience connecting to heterogeneous enterprise environmentsREST APIs, GraphQL, webhooks, EDI, SFTP, and enterprise middleware (MuleSoft, Boomi, or similar)Database connectors across SQL (Postgres, SQL Server, Oracle) and NoSQL (MongoDB, DynamoDB, Cosmos DB) .Airflow, Prefect, or Dagster for pipeline orchestrationPySpark or SQL-based batch processing at scaleData Platforms & Storage Cloud data warehouses: Snowflake, BigQuery, Redshift, Azure SynapseData lake/Lakehouse: Databricks, Delta Lake, Apache IcebergStreaming: Kafka, Kinesis, or Pub/Sub for real-time data flowsVector databases (pgvector, Pinecone, Weaviate) for AI-adjacent use casesSoftware Engineering FundamentalsPython (strong) — data engineering, scripting, SDK/library developmentInfrastructure as code: Terraform, Pulumi, or CloudFormationCI/CD for data pipelines — testing, versioning, deployment automationAPI design and SDK delivery so downstream teams consume data cleanly Enterprise & Customer ContextExperience deploying into customer-managed environments (not just SaaS)Understanding of enterprise data governance, compliance, and access control requirementsAbility to read an existing customer architecture and design around it — not replace it