Data Engineer

Attention Arc•Irvington, NY

9d•$75,000 - $90,000•Hybrid

About The Position

As part of our Technology & Analytics team, the Builders and Translators, you help design the systems that turn complexity into clarity and data into action. Your work connects strategy to execution, ensuring our insights are scalable, reliable, and ready to move the business forward. In this role, you will help modernize the infrastructure that powers planning, activation, attribution, and optimization across the agency, building systems that make teams faster, smarter, and more connected. As a Data Engineer, you will play a hands-on role in building, modernizing, and maintaining the data infrastructure that supports Attention Arc’s media and analytics ecosystem. You will partner across disciplines to create scalable systems, improve workflows, and ensure data can move efficiently from source to insight.

Requirements

2–5 years of experience in data engineering, ETL engineering, database development, or related technical roles
Strong command of SQL with hands-on experience in Microsoft SQL Server environments
Proven experience designing, building, and maintaining ETL/ELT pipelines and orchestrated workflows
Strong understanding of relational and dimensional data modeling principles
Experience working with Snowflake data warehouse environments, including data modeling, pipeline integration, performance optimization, and scalable analytics workflows
Familiarity with modern cloud-based data architecture and migration strategies, including Snowflake best practices for data transformation, orchestration, and governance
Working knowledge of scripting languages such as Python or PowerShell for automation and workflow optimization
Familiarity with JavaScript in the context of tagging, tracking, or data collection implementations
Experience working across Linux and Windows server environments
Strong understanding of data quality, system reliability, and technical documentation best practices
Ability to communicate technical concepts clearly and effectively to cross-functional stakeholders

Nice To Haves

Experience modernizing or refactoring legacy SSIS-based workflows
Exposure to cloud platforms such as Azure or AWS
Experience with SSAS, SSIS, or SSRS
Understanding of digital media data, attribution, and analytics ecosystems
Experience supporting machine learning or advanced analytics workflows
Familiarity with tag management systems, CDNs, or marketing pixel infrastructure
Relevant technical certifications such as MCSE or MCSA

Responsibilities

Design, build, and maintain scalable ETL/ELT pipelines that ingest and process data from internal systems, media platforms, and third-party vendors
Refactor and modernize legacy workflows to reduce complexity, improve reliability, and streamline onboarding and maintenance
Standardize data pipelines and integration frameworks to support scalable and repeatable client implementations
Manage and optimize workflow orchestration tools, including Airflow and related automation frameworks
Develop and maintain high-performance relational and analytical data models that support reporting, planning, and optimization workflows
Build and support Microsoft SQL Server environments and integrations with internal and external systems
Ensure best practices for database security, backup and recovery processes, performance monitoring, and data quality
Create and maintain reporting tables, views, and data structures used across the agency
Support tracking, tagging, and measurement infrastructure, including JavaScript-based implementations and vendor pixel management
Partner with vendors and internal stakeholders to troubleshoot attribution and measurement solutions
Support privacy-conscious data and measurement strategies, including modern attribution and identity frameworks
Automate repetitive workflows using scripting, orchestration, and tooling solutions to improve efficiency and consistency
Evaluate emerging platforms and technologies, including cloud services and modern data warehousing solutions, to assess scalability and business impact
Collaborate closely with analysts, planners, developers, and client teams to translate business needs into technical solutions
Establish and document best practices for data engineering, systems architecture, and workflow design
Maintain clear technical documentation and communicate effectively with both technical and non-technical stakeholders