Data Engineer

Job Title: Data Engineer Type: Full-time; Remote Schedule: EST Industry: Beauty About the Role We are seeking a hands-on Data Engineer to own and scale our in-house data and analytics capabilities as we transition away from an external agency.
This role will be foundational in building the modern data infrastructure and analytics layer that supports marketing performance, e-commerce insights, and broader business decision-making across a fast-growing DTC brand.
You will work closely with Marketing, Finance and Operations to ensure data is reliable, accessible, and actionable.
This is a highly visible role that blends data engineering, analytics enablement, and business partnership.
Responsibilities Own and operate our modern data stack (TBD), replacing external agency workflows with scalable in-house solutions Syncs: ○ Build integrations to sync data from the warehouse back into production tools like Klaviyo Build and maintain analytics-ready data models to support marketing analytics, e-commerce performance, customer behavior, and financial reporting ○ Manage our DBT model and business logic for the Marketing tracker, business unit reporting, attribution, etc.
○ Implementing updates when there are new data sources, changes to existing ones, new integrations and new questions that require new ways of viewing the data ○ Building new dashboards and editing existing ones in Looker, ensuring that they easy to use and understand with strong data visualizations Develop reporting packages, scorecards, and dashboards in Looker to support: ○ Marketing performance (paid media, CAC, ROAS, attribution, funnel metrics) ○ E-commerce and conversion metrics ○ Customer lifecycle, retention, and LTV ○ Inventory, operations, and revenue performance Partner closely with Marketing to enable deep, self-service marketing analytics across channels (Meta, Google, TikTok, email, SMS, affiliates, etc.
) Design and maintain reliable ELT pipelines using Snowflake for core business systems (e-commerce, marketing platforms, CRM, finance, and operations) ○ Build custom data pipelines to new data sources ○ Maintain existing custom pipelines Build and maintain dbt models to ensure clean, well-documented, and scalable analytics layers Translate business questions into data requirements and prioritized analytics deliverables Analyze historical and current performance trends to identify opportunities for optimization and growth Support reverse-ETL use cases (e.
g., syncing customer segments back into marketing and CRM tools) Ensure data quality, testing, monitoring, and documentation best practices Act as the primary in-house owner of analytics and reporting, reducing dependency on external partners Collaborate with Engineering and Technology teams to evolve Merit’s data architecture as the business scales 5+ years of experience in data engineering, analytics engineering, data analytics, or business intelligence Strong SQL expertise and experience with data programming languages such as Python or R Hands-on experience building data models and transformations using dbt Experience with cloud data warehouses, preferably Snowflake Experience with ELT tools such as Fivetran (or similar) Experience building LookML, views, and dashboards in Looker Experience with reverse-ETL tools such as Census (or similar) Strong understanding of marketing analytics and DTC metrics (CAC, ROAS, LTV, funnels, attribution, cohorts) Experience working with e-commerce platforms (e.
g., Shopify), marketing platforms, and customer data Proven ability to define and own analytics and data solutions end-to-end Strong analytical and problem-solving skills, with experience working on large datasets Excellent communication skills and ability to translate data into insights for non-technical stakeholders Comfortable operating in a fast-paced, high-growth environment with evolving priorities