Senior Data Engineer, Data Platform & Martech Engineering

Gen Digital

Confirmed live yesterday High trust

Remote

Quick summary

Work type: Remote
Location: New York, NY
Salary: $165,000–$187,000 / yr
Posted: 64 days ago
Freshness: Confirmed live yesterday
Nearby: 99+ roles within 25 mi

Market check

Salary context

Competitive pay

How this pay compares to similar roles

Similar $176k

This role $176k

$128k most similar roles pay here $224k

This role pays less than 52% of similar roles. Most pay $137,000–$215,100 — the shaded band above. At the midpoint, this role pays about $176k versus about $176k for comparable roles.

Based on 240 similar postings.

Employer

About Gen Digital

Gen Digital is a US-based consumer cybersecurity company formed by the merger of NortonLifeLock and Avast. It sells identity protection, antivirus, VPN, and privacy software under the Norton, Avast, AVG, Avira, LifeLock, ReputationDefender, and CCleaner brands.

Gen Digital currently has 23 open roles on FindRole.

Listed pay typically runs $172,240–$190,140 across 16 roles with salary data.

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View all roles at Gen Digital

At a glance

TL;DR · Senior Data Engineer, Data Platform & Martech Engineering

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Senior Data Engineer - Data Platform & Martech Engineering joins the MoneyLion Data Platform & Martech Engineering team to design, build, and scale data foundations for analytics, marketing activation, AI initiatives, and financial product experiences. This role involves developing batch and streaming pipelines, ETL/ELT frameworks, and enterprise-grade data models using Kimball dimensional modeling methodologies like star schemas and conformed dimensions. The engineer will manage Martech infrastructure involving tools such as Hightouch, Segment, Iterable, and Amplitude to enable audience orchestration and reverse ETL. Technical requirements include expert-level SQL, Python, Spark, or Java, alongside experience with Snowflake, Databricks, BigQuery, or Redshift. Candidates must also be proficient in Airflow, Dagster, dbt, and Kafka. The role focuses on solving complex business processes through scalable data contracts, ensuring high reliability, observability, and governance within a regulated financial services environment.

What does a Data Engineer earn?

Median $183138 from 214 postings across 58 companies.

See salary data

Skills

SQL Python Spark Java Snowflake Databricks BigQuery Redshift Airflow Dagster dbt Kafka Kinesis Pub/Sub Hightouch Segment Iterable Amplitude ETL ELT Dimensional Modeling Data Governance

What you'll do

Design and optimize scalable batch and streaming data pipelines for analytics, marketing, and product use cases.
Architect enterprise-grade data models and semantic layers using Kimball dimensional modeling methodologies.
Translate complex business processes into scalable data contracts and integration patterns with cross-functional teams.
Manage Martech and customer data infrastructure to enable audience orchestration and real-time marketing automation.
Build and operationalize AI-powered solutions and intelligent agents to improve internal productivity and customer engagement.
Enforce platform standards for data governance, lineage, metric consistency, and quality SLAs.
Improve operational excellence through monitoring, alerting, incident response, and root cause analysis.
Lead technical design discussions and mentor teammates on engineering best practices and AI-assisted development.

What we're looking for

Bachelor's degree in Computer Science, Engineering, Information Systems, Mathematics, or a related technical field required.
Master's degree (preferred).
5+ years of experience designing, building, and operating production-grade data platforms and distributed data systems.
Expertise in dimensional modeling, Kimball methodologies, and semantic layer architecture is required.
Proficiency in SQL and experience with Python, Spark, or Java for production-quality code.
Experience with cloud data platforms (Snowflake, Databricks, BigQuery, or Redshift) and orchestration tools like Airflow, Dagster, or dbt.
Experience supporting customer data platforms, marketing technology ecosystems, or data activation platforms (preferred).
Experience in fintech, financial services, adtech, or other highly regulated industries (preferred).

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