Onsite || Senior/Staff Data Engineer - Duluth, GA & Dallas TX (Face to Face Interview ) (Local Preferred)

Hello

Job Role: Senior/Staff Data Engineer

Location: Duluth, GA & Dallas TX (Face to Face Interview ) (Local Preferred)

Job 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.

Essential Duties

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, Skills, Abilities and Educational Requirements

Required

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.

Preferred

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.

Thanks and regards

Sonali Silswal Team Lead- Talent Acquisition

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