Data Engineer-Marketing Technology

Foxit•Alpharetta, GA

13h

About The Position

Foxit is seeking an experienced Data Engineer to manage the data pipelines that support its go-to-market systems. This role, while aligned with marketing priorities, will operate within the Business Applications & Data Analytics team, adhering to their development standards and architectural patterns. The primary focus will be on marketing-related data needs, collaborating with demand generation, product marketing, sales operations, and digital teams to understand their requirements and build/maintain the necessary pipelines and integrations. This is a hands-on role involving the construction and operation of data infrastructure connecting marketing automation platforms (HubSpot), CRM (Salesforce), data warehouses (Databricks), licensing systems, payment platforms, and other source systems. The work will directly enable marketing's capabilities in audience segmentation, attribution measurement, and data-driven campaign execution at scale.

Requirements

5+ years of experience in data engineering, with hands-on pipeline development and production operations.
Strong proficiency in SQL and Python/PySpark for data pipeline development.
Experience building and maintaining ETL/ELT pipelines using Databricks, dbt, Airflow, Azure Data Factory, or equivalent.
Hands-on experience with cloud data platforms - Databricks, Snowflake, BigQuery, or Redshift.
Solid understanding of dimensional data modeling - fact tables, dimension tables, schema design, and data warehouse concepts.
Experience with medallion or layered data architectures (raw → cleansed → business-ready), Kimball-style star schemas, and one-big-table approaches to data modeling.
Working knowledge of API integration patterns - REST, webhooks, OAuth, batch sync architectures.
Experience with CRM platforms (Salesforce, HubSpot, or similar), marketing automation systems, and CPQ/quoting tools (DealHub or similar).
Bachelor's degree in Computer Science, Information Systems, or equivalent industry experience.

Nice To Haves

Experience with Databricks (Delta Lake, PySpark, Unity Catalog).
Familiarity with HubSpot APIs and data model.
Experience with identity resolution and customer data deduplication across multiple source systems.
Exposure to marketing data concepts — lead scoring, audience segmentation, campaign attribution, lifecycle stages.
Experience with Azure cloud services (Azure Functions, Azure DevOps, Azure Data Factory).
Knowledge of data security and privacy practices, particularly regarding PII handling.
Experience with code review processes and development standards compliance in a collaborative data engineering team.

Responsibilities

Design, build, and maintain ETL/ELT pipelines, optimizing the existing medallion architecture (Bronze → Silver → Gold) to transfer data between source systems (Salesforce CRM, HubSpot, NetSuite, Stripe, DealHub, LMS) and the Databricks data warehouse.
Develop pipelines using PySpark and SQL within Databricks notebooks, adhering to established development standards for naming, project structure, and transformations.
Manage the data sync layer between Databricks and HubSpot, including inbound enrichment flows (license status, renewal dates, subscription state, firmographic data) and outbound marketing engagement data (email events, workflow enrollment, lifecycle changes).
Build and maintain Exchange layer pipelines to curate data for external systems, ensuring proper formatting and validation.
Construct and maintain scheduled batch jobs and event-driven integrations using APIs (REST, webhooks, OAuth).
Monitor pipeline health, implement alerting for failures and data quality issues, and manage incident response for sync disruptions.
Document data flows, integration architecture, and troubleshooting procedures.
Build and maintain dimensional models in Databricks (fact tables, dimension tables, bridge tables) according to data warehouse object definitions and naming standards.
Collaborate with stakeholders and data analysts to create curated, business-ready tables and datamarts with applied business logic, KPI calculations, and aggregations optimized for analytics and campaign activation.
Implement identity resolution and deduplication logic to create unified customer profiles from multiple source systems.
Establish data validation rules, quality checks, and monitoring to ensure data accuracy and freshness in marketing systems.
Normalize disparate data sources into clean, centralized schemas with proper type enforcement, deduplication, and null handling.
Ensure data infrastructure supports audience segmentation, including firmographic, behavioral, and engagement signals.
Build the data layer for lifecycle marketing, including triggered campaigns, dynamic journey branching, and personalization based on enriched customer profiles.
Support marketing and demand generation teams with reliable data for audience targeting in HubSpot.
Maintain data flows for email deliverability, subscription management, and suppression list synchronization.
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 handoffs from web properties to HubSpot and Databricks.
Integrate enrichment data from third-party providers (firmographic, intent, technographic) into automated workflows.
Build and maintain the data infrastructure for campaign attribution, channel performance analysis, and funnel reporting.
Ensure accurate data for conversion analytics, lead source tracking, and marketing ROI measurement.
Route marketing engagement data back into Databricks for cross-functional analysis to support centralized reporting.