Data Engineer-Marketing Technology

Jobgether; United States; Remote; USD 120,000 - 160,000; Full time

Benefits offered by this job

Data infra ownership

Databricks & Delta Lake stack

Cross-functional collaboration

Ownership of pipelines

Job summary

Jobgether is seeking a Data Engineer specialized in Marketing Technology to own data pipelines that connect business systems for data-driven marketing initiatives. You will design, build, and maintain ETL/ELT pipelines, integrate data from Salesforce, HubSpot, NetSuite, Stripe, and more into Databricks, and enable marketing analytics and campaign activation.

The role emphasizes production reliability, data quality, and collaboration with marketing, sales, IT, and analytics teams, with ownership

Qualifications

• 5+ years in data engineering, with production pipelines.
• Strong SQL and Python/PySpark for data processing.
• Experience with Databricks, dbt, Airflow, or ADF.
• Hands-on with cloud data platforms: Databricks, Snowflake, BigQuery, or Redshift.
• Solid dimensional modeling and data warehouse concepts.
• Experience with medallion architectures and star schemas.
• API integration patterns: REST, webhooks, OAuth, batch sync.
• Experience with Salesforce or HubSpot data integrations.

Responsibilities

• Design, build, and maintain ETL/ELT pipelines in a medallion architecture.
• Integrate data from Salesforce, HubSpot, NetSuite, Stripe, DealHub, LMS into Databricks.
• Develop transformations using Python, PySpark, and SQL per standards.
• Synchronize data between Databricks and HubSpot; enrich marketing data.
• Build Exchange layer pipelines for external consumption.
• Develop scheduled batch jobs and event-driven integrations via REST, webhooks, OAuth.
• Monitor pipelines, set up alerts, and lead incident resolution.
• Document data flows, architecture, procedures, and runbooks.
• Create dimensional models: facts, dimensions, bridges per warehouse standards.
• Collaborate with stakeholders to curate datasets and data marts.
• Implement identity resolution, deduplication, and data validation.
• Establish data quality, freshness checks, and null handling.
• Support audience segmentation using firmographic/behavioral signals.
• Enable lifecycle marketing with enriched profiles and triggered campaigns.
• Maintain data flows for email deliverability and suppression lists.
• Integrate website forms, lead capture, and enrichment providers.
• Support campaign attribution, funnel reporting, and ROI measurement.
• Collaborate across marketing, sales, IT, analytics to scale data solutions.

Skills

SQL

Python/PySpark

ETL/ELT pipelines

Databricks platform

dbt / Airflow / Airflow-like

Azure Data Factory

Snowflake / BigQuery / Redshift

API integrations (REST, webhooks, OAut

CRM data (Salesforce, HubSpot)

Dimensional data modeling

Education

Bachelor's degree in Computer Science, Information Systems, or related field

Tools

Databricks

dbt

Airflow

Azure Data Factory

Snowflake

BigQuery

Amazon Redshift

Delta Lake

Unity Catalog

HubSpot API

Salesforce API

Job description

Accountabilities:

As a Data Engineer specializing in Marketing Technology, you will own the development, operation, and continuous improvement of data pipelines that connect business systems and support data-driven marketing initiatives. Your responsibilities will include:

• - Design, build, and maintain ETL/ELT pipelines using a medallion architecture (Bronze, Silver, and Gold) to integrate data from Salesforce, HubSpot, NetSuite, Stripe, DealHub, learning management systems, and other sources into Databricks.
• - Develop data transformations using Python, PySpark, and SQL while following established engineering standards, architectural patterns, and code review processes.
• - Manage data synchronization between Databricks and HubSpot, enriching marketing records with licensing, renewal, subscription, and firmographic information while bringing engagement data back into the data warehouse.
• - Build and maintain Exchange layer pipelines that prepare, validate, and format data for consumption by external systems.
• - Develop scheduled batch jobs and event-driven integrations using REST APIs, webhooks, OAuth, and other relevant integration patterns.
• - Monitor pipeline performance, implement alerts for failures and data quality issues, and lead incident resolution when integrations break.
• - Document data flows, integration architecture, operational procedures, and troubleshooting runbooks.
• - Build and maintain dimensional data models, including fact, dimension, and bridge tables, following established warehouse standards.
• - Collaborate with stakeholders and analysts to create curated datasets and data marts that support business logic, KPI calculations, reporting, and campaign activation.
• - Implement identity resolution, deduplication, normalization, and validation processes to create consistent customer profiles across multiple systems.
• - Establish data quality checks, freshness monitoring, type enforcement, and null-handling rules to maintain reliable data across marketing platforms.
• - Develop the data infrastructure required for audience segmentation using firmographic, behavioral, and engagement signals.
• - Enable lifecycle marketing through enriched customer profiles, triggered campaigns, dynamic customer journeys, and personalized communications.
• - Maintain data flows supporting email deliverability, subscription preferences, and suppression list synchronization.
• - Integrate website forms, lead capture systems, and third-party enrichment providers into automated marketing and analytics workflows.
• - Support campaign attribution, channel performance analysis, funnel reporting, conversion analytics, lead source tracking, and marketing ROI measurement.
• - Collaborate with marketing, sales, IT, and analytics teams to translate business requirements into scalable, maintainable data solutions.

Requirements:

You are an experienced data engineering professional who combines strong technical expertise with a practical understanding of business needs. You take ownership of production pipeline reliability, value data accuracy, and can translate requests from non-technical stakeholders into robust engineering solutions. The ideal candidate will bring the following qualifications:

• - At least 5 years of experience in data engineering, including hands‑on development and production operation of data pipelines.
• - Strong proficiency in SQL and Python/PySpark for data processing and pipeline development.
• - Experience building and maintaining ETL/ELT workflows using Databricks, dbt, Airflow, Azure Data Factory, or equivalent tools.
• - Hands‑on experience with cloud data platforms such as Databricks, Snowflake, BigQuery, or Amazon Redshift.
• - Solid understanding of dimensional data modeling, including fact and dimension tables, schema design, and data warehouse concepts.
• - Experience with medallion or layered data architectures, Kimball‑style star schemas, and one‑big‑table modeling approaches.
• - Working knowledge of API integration patterns, including REST, webhooks, OAuth, and batch synchronization.
• - Experience with CRM platforms such as Salesforce or HubSpot, marketing automation systems, and CPQ or quoting tools such as DealHub or similar solutions.
• - Bachelor's degree in Computer Science, Information Systems, or a related field, or equivalent professional experience.

Preferred Qualifications:

• - Experience with Databricks technologies, including Delta Lake, PySpark, and Unity Catalog.
• - Familiarity with HubSpot APIs, data structures, and integration requirements.
• - Experience implementing identity resolution and customer deduplication across multiple source systems.
• - Understanding of marketing data concepts, including lead scoring, audience segmentation, campaign attribution, and customer lifecycle stages.
• - Experience with Azure cloud services such as Azure Functions, Azure DevOps, and Azure Data Factory.
• - Knowledge of data security and privacy practices, particularly the handling and protection of personally identifiable information (PII).
• - Familiarity with collaborative engineering workflows, code reviews, and compliance with development standards.
• - Strong problem‑solving skills, attention to detail, and a proactive approach to monitoring and maintaining production data systems.
• - Ability to communicate effectively with technical teams and non‑technical stakeholders across marketing, sales, and IT.

Benefits:

• - Opportunity to work on high‑impact data infrastructure that directly supports marketing performance and business growth.
• - Modern technology stack featuring Databricks, Delta Lake, PySpark, HubSpot, Python, and SQL.
• - The opportunity to contribute to a major data platform migration and help shape evolving engineering architecture.
• - Cross‑functional collaboration with marketing, sales, IT, and analytics teams.
• - A hands‑on role with meaningful ownership of production pipelines, integrations, and data quality.
• - An inclusive working environment that values diversity and encourages collaboration.