Data Engineer

Overview

Aquent has partnered with a pioneering leader in autonomous mobility and cutting-edge transportation technology to find an exceptional data professional ready to revolutionize factory-floor intelligence. In this high-impact role, you will have the unique opportunity to build critical data infrastructure from the ground up, directly influencing the production and scalability of next-generation vehicles. By bridging the gap between physical manufacturing operations and advanced cloud analytics, your work will empower real-time decision-making and drive the future of smart manufacturing.

About the Opportunity

We are seeking a highly skilled and hands-on data expert with deep manufacturing domain expertise to design, build, and maintain robust data pipelines for a cutting-edge manufacturing operations lakehouse. Working alongside a collaborative team of innovators, you will be responsible for building Change Data Capture (CDC) pipelines from scratch, transforming raw factory-floor, supply chain, and production data into actionable insights. This role offers the perfect blend of technical ownership, complex data modeling, and operational observability, allowing you to make a tangible footprint on the future of industrial automation.

What You Will Do

• Design and build batch and streaming Change Data Capture (CDC) pipelines from scratch, ingesting manufacturing operations data from SAP (MES, HANA, SLT), on-premises databases, AWS DMS, and industrial sources like PLCs.
• Build and maintain Bronze, Silver, and Gold (Medallion) layers and dimensional models tailored to manufacturing operations—including unit traceability, work orders, non-conformance reporting, assembly line station results, logistics, and first-pass yield.
• Develop incremental SQL and dbt models (including MERGE/upsert, SCD Type 2, and late-arriving data handling) and process semi-structured test and diagnostic payloads using Python and PySpark.
• Monitor pipeline health, data quality, and freshness SLAs using AWS CloudWatch alarms, log monitoring, Grafana dashboards, and automated data quality tests.
• Orchestrate production workflows using Apache Airflow and manage AWS ingestion infrastructure (S3, IAM, DMS, Kinesis) using Git-based CI/CD.
• Collaborate with cross-functional teams to build semantic layers that power business intelligence dashboards and downstream analytical applications.

What You Bring (Required)

• 5 to 10 years of hands-on data engineering experience.
• Proven experience building data pipelines within manufacturing, industrial, automotive, or semiconductor operations.
• Direct, hands-on experience working with SAP (ERP, MES, HANA, or SLT) and a deep understanding of factory-floor data concepts such as non-conformances, production testing/diagnostics, yield, work orders, or supply chain logistics.
• Deep, hands-on experience building Change Data Capture (CDC) pipelines from scratch across relational databases and operational/industrial sources.
• Strong proficiency in dimensional modeling, Medallion architecture (Bronze/Silver/Gold), and building semantic layers for analytics.
• Advanced programming skills in Python, PySpark, and SQL, along with production experience in AWS (S3, DMS, Kinesis, CloudWatch, IAM) and workflow orchestration (Airflow or similar).

Bonus Points (Preferred)

• Production experience with dbt, Amazon Redshift, Databricks, or Grafana.
• Experience structuring lakehouse data to support AI and agentic applications.