Data Engineer I
The Walt Disney Company (France), Santa Monica, CA, United States
About the Team
The Acquisition Marketing Engineering team owns the ingestion, modeling, and activation of acquisition and lifecycle marketing data across multiple platforms. We build and maintain large scale data pipelines that ingest vendor data (paid media, mobile attribution, search, social, display, email, and more), land it in cloud storage, and transform it into analytics‑ready datasets powering acquisition reporting tools, dashboards, and executive insights. You will be a member of the engineering team driving multiple transformations from end‑to‑end.
Job Summary
The Data Engineer will support the design, development, and maintenance of data pipelines and transformation workflows that power acquisition reporting and marketing analytics. You will work across AWS, Databricks, Unity Catalog, Snowflake, and Airflow to build reliable and scalable solutions for ingesting and preparing marketing platform data. You will collaborate with senior engineers, analytics partners, and marketing stakeholders to ensure data accuracy, consistency, and timely delivery for downstream dashboards and reporting. This role involves hands‑on development, troubleshooting, and contributing to the ongoing modernization of our data ecosystem.
Responsibilities and Duties of the Role
Assist in building and maintaining ETL/ELT pipelines for acquisition reporting using Databricks, PySpark, SQL, and Unity Catalog under the guidance of senior engineers.
Support the migration of existing Snowflake SQL scripts and transformations into Databricks UC by updating queries, validating outputs, and helping implement governance best practices.
Contribute to developing ingestion processes for marketing vendor data, including data parsing, normalization, and quality validations.
Implement and maintain foundational data quality checks, monitoring alerts, and issue triage workflows using Databricks, Snowflake, Airflow, and internal tooling.
Partner with the Data Reliability Engineering team to assist with SLA monitoring, simple incident troubleshooting, and logging improvements.
Collaborate with analytics and marketing partners to understand data requirements and ensure accuracy of datasets used in dashboards and reporting.
Support performance tuning, logging improvements, and general pipeline reliability work.
Participate in engineering best practices, including code reviews, documentation, and contributing to shared frameworks and tools.
Required Education, Experience/Skills/Training
Strong proficiency in SQL (analytical SQL, complex joins, window functions).
Hands‑on experience with PySpark and/or Spark SQL in production.
Good understanding of data modeling, ETL/ELT design patterns, and distributed data processing.
Experience building pipelines in Databricks, including Delta Lake, Unity Catalog, data governance, and Lakehouse patterns.
Experience in AWS (S3, IAM, EC2, Glue, Lambda, or related services).
Experience with Airflow or similar orchestration tools.
Experience building robust ingestion pipelines and working with semi‑structured formats (JSON, Parquet, CSV).
Experience with Git/GitHub, CI/CD, and modern DevOps practices.
Excellent communication skills and ability to work with cross‑functional partners.
Required Education
Bachelor’s degree in Computer Science, Information Systems, Software, Advanced Mathematics, Statistics, Data Engineering or comparable field of study, and/or equivalent work experience.
The hiring range for this position in Santa Monica, CA is $89,000 to $119,300 per year based on a 40 hour work week. The amount of hours scheduled per week may vary based on business needs. The base pay actually offered will take into account internal equity and also may vary depending on the candidate’s geographic region, job‑related knowledge, skills, and experience among other factors. A bonus and/or long‑term incentive units may be provided as part of the compensation package, in addition to the full range of medical, financial, and/or other benefits, dependent on the level and position offered.
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The Acquisition Marketing Engineering team owns the ingestion, modeling, and activation of acquisition and lifecycle marketing data across multiple platforms. We build and maintain large scale data pipelines that ingest vendor data (paid media, mobile attribution, search, social, display, email, and more), land it in cloud storage, and transform it into analytics‑ready datasets powering acquisition reporting tools, dashboards, and executive insights. You will be a member of the engineering team driving multiple transformations from end‑to‑end.
Job Summary
The Data Engineer will support the design, development, and maintenance of data pipelines and transformation workflows that power acquisition reporting and marketing analytics. You will work across AWS, Databricks, Unity Catalog, Snowflake, and Airflow to build reliable and scalable solutions for ingesting and preparing marketing platform data. You will collaborate with senior engineers, analytics partners, and marketing stakeholders to ensure data accuracy, consistency, and timely delivery for downstream dashboards and reporting. This role involves hands‑on development, troubleshooting, and contributing to the ongoing modernization of our data ecosystem.
Responsibilities and Duties of the Role
Assist in building and maintaining ETL/ELT pipelines for acquisition reporting using Databricks, PySpark, SQL, and Unity Catalog under the guidance of senior engineers.
Support the migration of existing Snowflake SQL scripts and transformations into Databricks UC by updating queries, validating outputs, and helping implement governance best practices.
Contribute to developing ingestion processes for marketing vendor data, including data parsing, normalization, and quality validations.
Implement and maintain foundational data quality checks, monitoring alerts, and issue triage workflows using Databricks, Snowflake, Airflow, and internal tooling.
Partner with the Data Reliability Engineering team to assist with SLA monitoring, simple incident troubleshooting, and logging improvements.
Collaborate with analytics and marketing partners to understand data requirements and ensure accuracy of datasets used in dashboards and reporting.
Support performance tuning, logging improvements, and general pipeline reliability work.
Participate in engineering best practices, including code reviews, documentation, and contributing to shared frameworks and tools.
Required Education, Experience/Skills/Training
Strong proficiency in SQL (analytical SQL, complex joins, window functions).
Hands‑on experience with PySpark and/or Spark SQL in production.
Good understanding of data modeling, ETL/ELT design patterns, and distributed data processing.
Experience building pipelines in Databricks, including Delta Lake, Unity Catalog, data governance, and Lakehouse patterns.
Experience in AWS (S3, IAM, EC2, Glue, Lambda, or related services).
Experience with Airflow or similar orchestration tools.
Experience building robust ingestion pipelines and working with semi‑structured formats (JSON, Parquet, CSV).
Experience with Git/GitHub, CI/CD, and modern DevOps practices.
Excellent communication skills and ability to work with cross‑functional partners.
Required Education
Bachelor’s degree in Computer Science, Information Systems, Software, Advanced Mathematics, Statistics, Data Engineering or comparable field of study, and/or equivalent work experience.
The hiring range for this position in Santa Monica, CA is $89,000 to $119,300 per year based on a 40 hour work week. The amount of hours scheduled per week may vary based on business needs. The base pay actually offered will take into account internal equity and also may vary depending on the candidate’s geographic region, job‑related knowledge, skills, and experience among other factors. A bonus and/or long‑term incentive units may be provided as part of the compensation package, in addition to the full range of medical, financial, and/or other benefits, dependent on the level and position offered.
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