[Hiring] Senior/Staff Data Engineer @Apptad, Inc.

Role Description

The Senior/Staff Data Engineer exists to design, build, and maintain the foundational data platform infrastructure that enables analytics, data science, AI, and application teams to access reliable, governed, and well-structured data. This role is responsible for managing the organization's Snowflake-based data lake, sustaining the Medallion data architecture, and ensuring data flows securely and efficiently from source systems across the enterprise.

• Design, develop, and maintain scalable data pipelines that ingest and transform data from multiple ERP and source systems into the organization's data lake, adhering to data architecture standards across layers.
• Build and publish curated, well-documented data products in the business layer that serve Analytics, Business Intelligence, Data Science/ML, AI, and application use cases across the organization.
• Administer the data platform including database objects, virtual warehouses, role-based access controls, resource monitors, and security configurations to ensure a performant, cost-efficient, and secure environment.
• Implement and sustain Data Governance practices across the platform including data quality frameworks, metadata cataloging, lineage tracking, and access policy management.
• Partner with cross-functional stakeholders, including Analytics, Data Science, IT, Finance, Operations, and Application Engineering teams, to gather data requirements and deliver reliable data products that support business decision-making.
• Monitor, troubleshoot, and continuously improve platform reliability, pipeline performance, and data quality across all layers of the data lake.
• Establish and maintain engineering standards, technical documentation, and CI/CD practices for data pipeline development and platform operations.

Qualifications

• Bachelor's degree in Computer Science, Information Systems, Engineering, Mathematics, or a related technical field; equivalent practical experience will be considered in lieu of a degree.
• 5+ years of professional experience in data engineering, with hands-on ownership of production data pipelines and platform infrastructure.
• Advanced proficiency in SQL and Python for data pipeline development, transformation, and automation.
• Deep hands-on experience with Snowflake, including warehouse management, RBAC, query optimization, Tasks, Streams, Stages, and Snowpark.
• Demonstrated experience designing and maintaining a Medallion or equivalent layered data architecture in a cloud data lake environment.
• Experience integrating data from multiple heterogeneous source systems, including ERP platforms or transactional databases.
• Experience building datasets for diverse consumers including BI/analytics, data science, ML/AI, and application teams.
• Working knowledge of broad data engineering tooling including Apache Spark, dbt, Apache Airflow, or equivalent frameworks.
• Experience implementing Data Governance practices including access control, data quality, lineage tracking, and metadata management.
• Strong communication and stakeholder engagement skills, with the ability to translate technical concepts for non-technical audiences.
• Proficiency with Git and CI/CD practices as applied to data engineering workflows.

Requirements

• 8+ years of data engineering experience with demonstrated technical leadership or staff-level platform ownership.
• Experience in manufacturing, distribution, or industrial B2B environments.
• Familiarity with Snowflake Cortex, Snowpark ML, or other ML-adjacent platform capabilities.
• Experience operating in a multi-ERP source environment with complex entity resolution or data integration challenges.
• Exposure to data mesh, data contract, or data product frameworks.
• Experience with Tableau or similar BI platforms from a data provider/platform perspective.
• Familiarity with private equity-backed company environments and the pace and expectations that come with them.

Key Competencies

• Technical Depth - Possesses advanced, hands-on expertise across the full data engineering stack; approaches platform problems with rigor and translates that depth into reliable, production-grade solutions.
• Platform Ownership - Takes end-to-end accountability for the health, performance, and evolution of the data platform; proactively identifies risks and drives resolution without waiting to be directed.
• Data Quality Mindset - Treats data as a product; consistently applies quality standards, governance practices, and documentation discipline to ensure platform consumers can trust what they receive.
• Stakeholder Partnership - Builds effective working relationships across technical and business teams; listens to understand needs, communicates transparently, and delivers solutions that address the underlying business problem.
• Problem Solving - Diagnoses complex pipeline failures, data anomalies, and performance issues methodically; brings structured thinking and creativity to both technical and process challenges.
• Adaptability - Thrives in dynamic, greenfield environments where priorities shift and patterns are still being established; comfortable with ambiguity and capable of making sound decisions with incomplete information.
• Collaboration - Works effectively across organizational boundaries; contributes generously to team knowledge sharing, documentation, and the success of adjacent teams.
• Continuous Improvement - Consistently looks for opportunities to improve platform reliability, engineering practices, and team effectiveness; stays current on relevant tools, frameworks, and industry patterns.