Data Solutions Engineer
AI Digital · Remote job · posted Aug 7, 2026
What this role actually asks for
Extracted by RemoteHuntMust have
- •Design and implement automation workflows
- •Build and maintain operational integrations
- •Lead incident investigations and root cause analysis
- •Mentor junior colleagues
- •Manage version control and code reviews
- •Write clean, modular, and scalable code
- •Develop and maintain documentation frameworks
Nice to have
- •Experience with ClicData, Slack, Google Sheets, or marketing platforms
Tools and technologies
The full posting
AI Digital is looking for a Middle/Senior Data Solutions Engineer who will be responsible for designing, scaling, and maintaining robust automation frameworks and operational data integrations across multiple platforms. This role will focus on driving efficiency, reliability, and scalability while ensuring high standards of technical documentation and operational excellence. The Senior Data Solutions Engineer will also mentor and support junior engineers and contribute to continuous process improvement and technical innovation across the Data Solutions team. Responsibilities: Design and implement complex automation workflows across BigQuery, BI systems, and other tools. Build and maintain operational integrations that connect various tools and systems directly (e.g., ClicData, Slack, Google Sheets, or marketing platforms). Lead major incident investigations, conduct root cause analyses, and implement preventive improvements and recovery mechanisms. Drive analytical and critical thinking in data initiatives; challenge assumptions and propose innovative solutions. Mentor junior colleagues. Manage version control, code reviews, and collaborative development via GitHub; lead debugging, monitoring, and optimization of automation pipelines. Collaborate with the engineering team to ensure automations align with reporting and system needs. Write and maintain clean, modular, and scalable code; establish automated testing. Develop and maintain comprehensive documentation frameworks; create reusable guides and ensure transparency across projects. Demonstrate exceptional attention to detail and establish team-wide quality standards; contribute to and align data strategies with organizational goals, influencing long-term data roadmap and architecture decisions.
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