Data Platform Engineer

Build and operate the data pipelines, warehouses, and transformation layers that form the analytical and operational data foundations for enterprise clients.

Quix is looking for a Data Platform Engineer to design and maintain the data infrastructure that enterprise analytics, AI workflows, and operational reporting depend on. This role is for someone who brings engineering discipline to data pipeline design, transformation layer management, data quality enforcement, and the governance structures that make enterprise data trustworthy and accessible.

Work is calm, technical, and delivery-focused. You’ll help teams make durable decisions in enterprise environments where reliability and operational clarity matter.

What You’ll Do

• Design and build data ingestion pipelines from diverse enterprise sources: databases, APIs, event streams, and file systems.
• Develop transformation layers using dbt, SQL, or Python that produce clean, well-documented analytical models.
• Manage data warehouse and lake architectures: Snowflake, BigQuery, Databricks, or Azure Synapse.
• Implement data quality checks, schema validation, and data contract enforcement at pipeline boundaries.
• Establish data catalog practices, lineage documentation, and metadata management.
• Collaborate with ML engineers and analytics teams to ensure data products meet downstream requirements.
• Monitor pipeline reliability, freshness, and performance, and resolve incidents systematically.

What We’re Looking For

• Strong production experience building and operating data pipelines and transformation layers.
• Proficiency with SQL and at least one data orchestration framework: Airflow, dbt, Prefect, Dagster, or equivalent.
• Deep experience with at least one cloud data warehouse or lake platform: Snowflake, BigQuery, Databricks, or Synapse.
• Understanding of data modeling patterns: dimensional modeling, data vault, or equivalent.
• Experience with data quality frameworks and systematic testing of data pipelines.
• Ability to document data models, pipeline logic, and data contracts clearly for technical and analytical audiences.

Nice to Have

• Experience with streaming data platforms: Kafka, Kinesis, Pub/Sub, or Azure Event Hubs.
• Background in data mesh or data product ownership models.
• Experience implementing data governance in regulated environments: PIPEDA, GDPR, or SOC 2.

Enterprise decisions, AI models, and operational reporting are only as reliable as the data that feeds them. Data platform engineering that emphasizes quality, governance, and operational reliability directly improves the analytical confidence and decision‑making capability of the organizations Quix serves.