layiq

/la·yiq/adjective

worthy; deserving; fitting; suitable.

A role, opportunity, or path that merits attention, time, and pursuit.

Data Engineer-Marketing Technology at Foxit | LAYIQ

Data Pipeline Development & Operations
Design, build, and maintain ETL/ELT pipelines, building upon and further optimizing our existing medallion architecture (Bronze → Silver → Gold) to move data between source systems (Salesforce CRM, HubSpot, NetSuite, Stripe, DealHub, LMS) and our Databricks data warehouse.
Build pipelines using PySpark and SQL in Databricks notebooks, following established development standards for naming, project structure, and layer-appropriate transformations.
Own the data sync layer between Databricks and HubSpot — enrichment flows inbound to HubSpot (license status, renewal dates, subscription state, firmographic data) and marketing engagement data flowing back to Databricks (email events, workflow enrollment, lifecycle changes).
Build and maintain Exchange layer pipelines that curate data for external system consumption, formatting and validating data to meet target system requirements.
Build and maintain scheduled batch jobs and event-driven integrations using APIs (REST, webhooks, OAuth).
Monitor pipeline health, set up alerting for failures and data quality degradation, and own incident response when syncs break.
Maintain documentation of data flows, integration architecture, and troubleshooting runbooks.
Data Modeling & Quality
Build and maintain dimensional models in Databricks (fact tables, dimension tables, bridge tables) following our data warehouse object type definitions and naming standards.
Work in collaboration with stakeholders and data analysts to build curated, business-ready tables and datamarts that apply business logic, KPI calculations, and aggregations optimized for analytics and campaign activation.
Implement identity resolution and deduplication logic to produce unified customer profiles from multiple source systems.
Establish data validation rules, quality checks, and monitoring to ensure accuracy and freshness of data flowing into marketing systems.
Normalize disparate data sources into clean centralized schemas with proper type enforcement, deduplication, and null handling.
Marketing Data & Segmentation Support
Ensure the data infrastructure supports audience segmentation, including firmographic, behavioral, and engagement signals.
Build the data layer that powers lifecycle marketing - triggered campaigns, dynamic journey branching, and personalization based on enriched customer profiles.
Support marketing and demand gen teams with reliable, accessible data for building audience targets in HubSpot.
Maintain data flows for email deliverability, subscription management, and suppression list synchronization.
Integration Development
Build and maintain API integrations between marketing, sales, and operational systems using Python and SQL.
Implement field-level transformation logic, sync orchestration, and error handling for system-to-system data flows.
Support website form and lead capture data flows - ensuring clean handoff from web properties into HubSpot and Databricks.
Work with third-party enrichment providers (firmographic, intent, technographic) to integrate enrichment data into automated workflows.
Reporting & Attribution
Build and maintain the data infrastructure that supports campaign attribution, channel performance analysis, and funnel reporting.
Ensure accurate data for conversion analytics, lead source tracking, and marketing ROI measurement.
Support centralized reporting by routing marketing engagement data back into Databricks for cross-functional analysis.

Data Engineer-Marketing Technology

Employer job description

About Us:

About the Role

What You'll Do

What You Bring

What Sets You Apart

Why Join Us

Equal opportunity and accommodations

About this listing