Data Engineer

Attention Arc•Durham, NC

10h•$80,000 - $120,000

About The Position

At Attention Arc, we exist to make media matter. As part of our Technology & Analytics team — The Builders and Translators — you architect the systems that turn raw data into insight and insight into action. Your work connects planning, activation, attribution, and optimization — ensuring every impression is measurable, every dataset is trusted, and every system moves us forward. This Data Engineer role specializes in client support and infrastructure modernization. You will maintain and strengthen our existing data ecosystem while thoughtfully evolving it for what’s next. It’s a hands-on, mid-level role with real ownership — ideal for someone who thrives on solving complex problems, improving legacy systems, and building scalable infrastructure that makes the entire agency smarter and faster. You won’t just keep systems running. You’ll help raise the bar for how we build, support, and innovate.

Requirements

2–5 years of experience in data engineering, database development, or ETL engineering
Advanced SQL skills with hands-on Microsoft SQL Server experience
Proven experience designing, building, and maintaining ETL/ELT pipelines and orchestrated workflows
Strong understanding of relational and dimensional data modeling
Experience with scripting languages such as Python or PowerShell
Working knowledge of JavaScript in tracking, tagging, or data collection contexts
Comfort working across Windows and Linux environments
Experience implementing data quality, validation, and monitoring frameworks
Ability to translate technical complexity into clear, actionable communication
Strong documentation habits and disciplined version control practices
A structured, methodical approach to debugging and problem solving
Comfort balancing reactive support needs with proactive system improvements

Nice To Haves

Experience modernizing SSIS-based workflows
Exposure to Snowflake or cloud data warehouses
Experience with Azure or AWS environments
Familiarity with SSAS, SSIS, or SSRS
Understanding of digital media data, attribution modeling, and analytics
Experience collaborating on machine learning workflows
Tag management, CDN, or marketing pixel implementation experience

Responsibilities

Maintain and optimize Microsoft SQL Server databases and core data infrastructure
Document existing systems and design clear, phased migration strategies from legacy workflows to modernized environments
Refactor outdated processes to reduce friction, errors, and onboarding time
Ensure best practices in database security, backup/recovery, performance tuning, and monitoring
Build stable, high-performance relational and analytical data models
Design, build, and maintain scalable ETL/ELT pipelines ingesting data from internal systems, media platforms, and third-party vendors
Standardize pipelines to enable repeatable, efficient client integrations
Manage and optimize workflow orchestration tools (e.g., Airflow) to improve reliability and performance
Create and maintain reporting tables, views, and structures used across the agency
Partner directly with client teams to design and deliver custom reporting solutions
Troubleshoot infrastructure and data issues quickly and thoughtfully
Translate complex technical concepts into clear, actionable explanations
Balance responsive client support with long-term system improvement
Support and enhance tracking and pixel infrastructure, including JavaScript-based implementations
Improve tagging and vendor pixel management through automation and tooling (e.g., tag managers, CDNs)
Collaborate with partners (e.g., Google and other platforms) to implement advanced attribution solutions
Enable privacy-aware measurement approaches, including hashed identifiers and modern attribution techniques
Automate repetitive workflows using scripting and orchestration tools
Evaluate and prototype new technologies (e.g., Snowflake, Azure, AWS) to assess business impact
Improve analytics and machine learning workflows by increasing consistency and reducing manual effort
Proactively identify inefficiencies and recommend thoughtful, scalable improvements
Work cross-functionally with analysts, planners, activation teams, and developers to translate business needs into technical systems
Establish and document best practices for data engineering and infrastructure design
Maintain clear, accessible technical documentation and training materials