Skillset: Enterprise Data Architecture, Azure data bricks. Integrations · Multi-Layer Platform. Data layer design We 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 products Map 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 source Define the canonical data models our products are built on — consistent, versioned, and documented Work with product and AI teams to ensure data is structured correctly for downstream agent and analytics use cases Own data quality — schema validation, lineage tracking, and monitoring across all pipelines Guide technical decisions on storage, processing, and integration tooling as the platform scales
Responsibilities
Data Architecture & Modeling Designing multi-layer data architectures (raw → curated → serving) Canonical schema design, data contracts, and versioned data models Data vault, dimensional modeling, or Lakehouse patterns depending on context
Integration & Connectivity & Orchestration Deep experience connecting to heterogeneous enterprise environments REST APIs, Graph QL, webhooks, EDI, SFTP, and enterprise middleware (Mule Soft, Boomi, or similar) Database connectors across SQL (Postgres, SQL Server, Oracle) and NoSQL (Mongo DB, Dynamo DB, Cosmos DB) . Airflow, Prefect, or Dagster for pipeline orchestration PySpark or SQL-based batch processing at scale
Data Platforms & Storage Cloud data warehouses: Snowflake, Big Query, Redshift, Azure Synapse Data lake/Lakehouse: Databricks, Delta Lake, Apache Iceberg Streaming: Kafka, Kinesis, or Pub/Sub for real-time data flows Vector databases (pgvector, Pinecone, Weaviate) for AI-adjacent use cases
Software Engineering Fundamentals Python (strong) — data engineering, scripting, SDK/library development Infrastructure as code: Terraform, Pulumi, or Cloud Formation CI/CD for data pipelines — testing, versioning, deployment automation API design and SDK delivery so downstream teams consume data cleanly
Enterprise & Customer Context Experience deploying into customer-managed environments (not just Saa S) Understanding of enterprise data governance, compliance, and access control requirements Ability to read an existing customer architecture and design around it — not replace it