Data Engineer

MARICI Solutions GmbH; Santa Monica (CA); Hybrid; USD 120,000 - 180,000; Full time

Job summary

MARICI Solutions GmbH in Santa Monica is seeking a Data Engineer to design and maintain ETL/ELT pipelines using Databricks, PySpark, and SQL. You will help migrate Snowflake scripts to Databricks UC, implement governance, and build robust ingestion processes.

You will collaborate with analytics and marketing teams to ensure dataset accuracy for dashboards, while contributing to reliability and performance improvements across the data stack.

Qualifications

• 4+ years of experience as a Data Engineer or similar role.
• 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.

Responsibilities

• 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 basic 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.

Skills

SQL proficiency

PySpark / Spark SQL

Data modeling & ETL design

Databricks Lakehouse

Airflow orchestration

Tools

Databricks

Delta Lake

Unity Catalog

Airflow

AWS

Job description

Role: Data Engineer

Fulltime

• 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 basic 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.

Location: Santa Monica, Burbank, or Glendale (Hybrid, 4 days onsite; 1 day remote)

Fulltime

Key Responsibilities

• 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 basic 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.

Basic Qualifications

• 4+ years of experience as a Data Engineer or similar role.
• 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.

Based on the job description, tell me what the top 5 must have skills needed for this role?

• 4+ years of experience as a Data Engineer or similar role.
• 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.