Data Architect, Data Platform (Azure)

CargoSprint

3d•Remote

About The Position

This role is responsible for our data platform and data delivery. You will design and build our data warehouse, data marts, and reporting layer to support product, finance, operations, and customer-facing reporting. You'll own the architecture, standards, and execution plan across an Azure data platform (or similar).

Requirements

8+ years in data engineering, analytics engineering, or platform engineering with architecture ownership.
Proven experience building data warehouses and marts that support business reporting at scale.
Strong SQL and data modeling skills (dimensional modeling, star schemas, and/or semantic models).
Experience with Azure data platforms (Synapse/Fabric, ADLS, Databricks on Azure, ADF) or comparable equivalents (Snowflake, BigQuery, Redshift).
Experience with orchestration and transformation tooling (ADF, Airflow, dbt, Dagster, or equivalent).
Track record of modernization with incremental migration and clear deprecation plans.
Ability to align engineering and business stakeholders around shared definitions and priorities.

Nice To Haves

Experience with BI layers and semantic modeling (Power BI preferred; Tableau/Looker also fine).
Governance/catalog tooling and PII controls.
Streaming/event-driven data patterns (Kafka/Kinesis/PubSub) or CDC experience.
Payments, billing, invoicing, or other high-volume transactional domains.
Logistics, cargo, or supply chain experience.
Spanish language proficiency.

Responsibilities

Own the design and delivery of our data warehouse and data marts: models, schemas, semantic consistency, and documentation.
Build and evolve reporting foundations: curated datasets, metric definitions, and repeatable reporting patterns.
Partner with stakeholders to define source-of-truth metrics and ensure consistent definitions across teams.
Establish standards for data quality, testing, reconciliation, lineage, access control, and lifecycle management.
Drive modernization: reduce legacy data debt, simplify flows, and improve reliability and performance across the platform.
Define data contracts with application teams (schemas, events/CDC patterns) so downstream reporting is stable.
Enable self-serve analytics by making data discoverable, documented, and safe to use.