Data Engineer: ETL, Snowflake & ML Pipelines

genlogs-corporation; United States; On-site; USD 110,000 - 170,000; Full time

Benefits offered by this job

Healthcare

Unlimited PTO

Parental leave

PD budget

Travel reimbursement

401(k)

Job summary

GenLogs is a transportation-technology company seeking a data engineer to design and maintain scalable ETL/ELT pipelines, ingest workflows, and analytics-ready data for ML and BI applications.

You will work with Python, SQL, Snowflake, Airflow, and dbt to ensure data quality, performance, and reliable operation, collaborating with engineering and data teams in a fast-paced environment.

Qualifications

• 3+ years of experience in data engineering or software engineering.
• Strong Python and SQL skills.
• Hands-on experience with Snowflake, AWS Firehose/S3, Airflow, and dbt.
• Ability to work independently and execute in a dynamic environment.
• Strong problem-solving skills and attention to detail.

Responsibilities

• Build and maintain ETL/ELT pipelines using Python and SQL.
• Develop ingestion workflows with AWS Firehose, S3, and related services.
• Create and optimize dbt models, tests, and incremental logic.
• Tune Snowflake queries and warehouse usage for cost and performance.
• Operate and improve Airflow DAGs for reliable execution and monitoring.
• Maintain high data quality, data integrity, and pipeline SLA commitments.
• Bring clarity to ambiguous requirements and propose practical solutions.
• Build feature pipelines to support ML workflows.
• Support model deployment, monitoring, and automated retraining.
• Add data validation and quality checks across ML and analytics pipelines.

Skills

Python

SQL

Independent

Problem-solving

Attention to detail

Tools

Snowflake

AWS Firehose/S3

Airflow

dbt

Job description

GenLogs is a transportation-technology company seeking a data engineer to design and maintain scalable ETL/ELT pipelines, ingest workflows, and analytics-ready data for ML and BI applications.

You will work with Python, SQL, Snowflake, Airflow, and dbt to ensure data quality, performance, and reliable operation, collaborating with engineering and data teams in a fast-paced environment.