Data Engineer

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Company Overview

A technology company is bringing advanced autonomous systems into the built environment by deploying AI-powered technology on heavy construction equipment. Its team combines experienced engineers and construction industry professionals to solve complex physical-world challenges, improve job-site safety, and accelerate critical infrastructure projects.

The organization has rapidly expanded its autonomous construction technology operations, securing significant funding and achieving fully autonomous excavator deployments. The team focuses on applying AI and robotics to real-world construction environments where practical engineering solutions can directly impact customers and infrastructure projects.

The organization is seeking a Data Engineer to help scale and strengthen its growing Data Platform. This role will work across the data engineering stack, including ingestion pipelines, ETL development, monitoring and alerting, data quality, and infrastructure enablement.

The successful candidate will work hands-on with data pipelines, query optimization, infrastructure reviews, and data platform improvements. This position plays a critical role in ensuring that data remains accurate, reliable, observable, and scalable as data volumes and business requirements continue to grow.

Key Responsibilities

• Design, build, and maintain robust and scalable data pipelines and ingestion workflows across a growing Data Lake.
• Define and enforce data quality standards, SLOs, and validation frameworks to ensure the accuracy and reliability of critical data assets.
• Continuously optimize existing pipelines for performance and cost efficiency as data volumes increase.
• Expand and maintain monitoring and alerting coverage to identify data issues before they become customer-facing problems.
• Establish best practices for data modeling, partitioning, and compute resource utilization.
• Support internal teams in building and maintaining reliable data infrastructure.
• Review existing data infrastructure and identify opportunities for improved scalability, reliability, and efficiency.
• Help develop data systems that support customer-facing experiences and maintain high levels of trust in data quality.
• Work hands-on with data pipelines, debugging, query optimization, and technical problem-solving.
• Contribute to the development of data infrastructure supporting autonomous construction systems.

Required Qualifications

• 5+ years of experience in data engineering, with a strong track record working in large-scale data lake or data warehouse environments.
• 5+ years of experience working with SQL and distributed query engines such as Spark, BigQuery, Snowflake, or similar technologies.
• Deep proficiency with pipeline orchestration tools such as Airflow, Prefect, or equivalent platforms.
• Strong experience with data transformation frameworks, including Spark or similar technologies.
• Experience designing and implementing data quality frameworks covering validation, anomaly detection, and lineage tracking.
• Familiarity with observability tools and practices for data systems, including monitoring, alerting, and incident response.
• Experience enabling non-engineering stakeholders to self-serve on data infrastructure through documentation, tooling, or hands-on support.

Preferred Qualifications

• Hands-on experience with Databricks and Spark.
• Experience with streaming or near-real-time data ingestion patterns.
• Familiarity with data governance and access control at scale.
• Background working on customer-facing data products or systems with external SLAs.

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