Data Engineer – Client Data Operations

Job not on LinkedIn

🔥 0 minutes ago

🇺🇸 United States – Remote

💵 $115k - $145k / year

⏰ Full Time

🟡 Mid-level

🟠 Senior

🚰 Data Engineer

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Limetree

WebsiteLinkedInAll Job Openings

11 - 50 employees

🏥 Healthcare

🏨 Hospitality

💼 Consulting

Healthcare • Hospitality • Consulting

Limetree is a behavioral marketing agency that provides end-to-end solutions designed to maximize an organization’s reach, performance, and budget by using science-based approaches. The company specializes in understanding customer behavior through data science and behavioral science, allowing businesses to make informed and strategic marketing decisions. Limetree helps organizations to stop relying on assumptions and start using scientific insights to truly understand their market, thus improving marketing outcomes and boosting ROI. Their expertise spans various industries like healthcare, retail, hospitality, and non-profit sectors, where they help optimize marketing strategies by understanding the psychological and contextual factors that influence customer decisions.

📋 Description

• Set up new feeds from client and vendor systems, including SFTP and scheduled file deliveries • Identify source fields, changes, missing data, and inconsistent formats • Map incoming data into established schemas and tables, identify sensitive fields, and apply approved privacy and access rules • Build validation and matching steps, including record counts, identity matching or LTID assignment, and CASS/NCOA processing where relevant • Document feed inputs, transformations, checks, outputs, and failure procedures • Build and extend SQL, Python, and Spark pipelines for direct mail and digital clients • Set up scheduled jobs, file watchers, transformations, testing, and monitoring • Improve existing pipelines as source files, schemas, tools, and business requirements change • Perform daily checks and troubleshoot Databricks workflows, AWS jobs, and related tools • Investigate failures and fix root causes, including schema drift, bad source files, expired credentials, broken dependencies, stale data, and vendor-tool issues • Improve alerts, recovery steps, and runbooks • Own routine audience and mail-drop production, including suppressions, address hygiene, count reconciliation, QC, and vendor file preparation • Flag risks to scheduled client drops and recommend safe resolutions • Hand off reliable, documented data to the Senior Analytics Engineer for downstream modeling, analysis, and reporting • Partner with campaign, account, and technical colleagues to clarify requirements and resolve issues

🎯 Requirements

• Several years of hands-on data engineering experience, ideally four or more, including pipelines operated in production and personally diagnosed failures • Comfort taking messy or poorly documented source files through reliable production data feeds • Strong attention to detail and ability to reconcile counts, question suspicious data, and prevent recurring problems • Ability to work independently, explain technical issues plainly, and identify when business-rule or privacy questions need an owner • Strong SQL and Python skills • Practical experience with Spark or similar large-scale data processing • Production cloud data engineering experience; Databricks and AWS or comparable experience • Experience with orchestration, monitoring, schema mapping, file-based integrations, access controls, Git, testing, and operational documentation • Careful handling of personally identifiable or protected health information in regulated data environments • Direct mail and/or digital marketing audience data experience is helpful, including deduplication, suppressions, CASS/NCOA, AccuZIP, AccuTrace, USPS tracking, or digital marketing sources and APIs • Healthcare, Medicare, or health plan marketing data; SFTP, S3, Parquet, and client-facing reporting feeds are helpful backgrounds

🏖️ Benefits

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