Senior Data Engineer — Data Pipelines & Analytics (Hybrid)

Nissan Motor Iberica SA; Franklin (TN); Hybrid; USD 100,000 - 140,000; Full time

Benefits offered by this job

Health insurance

401(k) matching

Job summary

Nissan in Franklin, TN is seeking a Data Engineer to design, build, and maintain data pipelines, ingest data from multiple sources, and optimize data for analytics. The role emphasizes data quality, reliability, and performance, with collaboration across Data Scientists and BI teams.

You will support MLOps and analytics workloads while documenting data flows and ensuring scalable, robust data architectures to power insights across the organization.

Qualifications

• Bachelor’s degree plus 6 years of experience in data engineering.
• Advanced SQL skills and proficiency in Python and Snowflake.
• Experience with data platforms including Snowflake, Databricks, BigQuery, and/or Redshift.
• Experience with ETL/ELT tools including ADF, Airflow, and/or dbt.
• Experience with streaming technologies including Kafka, Event Hubs.

Responsibilities

• Design, build, and maintain data pipelines (batch and streaming).
• Ingest data from multiple source systems.
• Transform and optimize data for analytics and BI.
• Ensure data quality, reliability, and performance.
• Implement data pipeline monitoring and alerting.
• Collaborate with Data Scientists and BI teams.
• Support MLOps and analytics workloads.
• Document data flows.

Skills

Advanced SQL

Python

Data analysis

Education

Bachelor’s degree in Computer Science, Data Science, or related field

Master’s degree in Data Science, Computer Science, or related field

Tools

Snowflake

Databricks

BigQuery

Redshift

ADF

Airflow

dbt

Kafka

Event Hubs

Job description

Nissan in Franklin, TN is seeking a Data Engineer to design, build, and maintain data pipelines, ingest data from multiple sources, and optimize data for analytics. The role emphasizes data quality, reliability, and performance, with collaboration across Data Scientists and BI teams.

You will support MLOps and analytics workloads while documenting data flows and ensuring scalable, robust data architectures to power insights across the organization.