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Remote role, common Data Engineer title, and mid-level scope increase candidate competition despite niche Meta Ads skill.
Core data engineering skills are transferable but Meta Ads and marketing attribution expertise increase industry specificity.
Multiple mandatory technical requirements (Python, advanced SQL, Airflow, BigQuery, Meta Ads expertise) create strict screening filters.
Design, build, and maintain scalable data pipelines and architectures primarily focused on Meta Ads and marketing data.
Own end-to-end data use cases including lead funnel optimization, campaign attribution, and revenue reporting with measurable business impact.
Collaborate closely with marketing, growth, and product teams to translate business requirements into data solutions and ensure data trust and usability.
Strong proficiency in Python and advanced SQL skills for large-scale data processing.
Hands-on experience with data ingestion from APIs, data orchestration tools (e.g., Airflow), and familiarity with cloud data platforms like BigQuery.
Solid understanding of Meta Ads platform fundamentals including campaign structure, attribution models, and lead generation workflows.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in building scalable, modular microservices-style data systems focused on marketing and ad platform data.
Able to own full data product lifecycles impacting business outcomes such as revenue and lead quality.
Strong analytical understanding of ad performance metrics and ability to interpret complex marketing data beyond surface metrics.