Senior Data Engineer: Build Data Pipelines & Lakehouse

Vericast; San Antonio (TX); On-site; USD 130,000 - 150,000; Full time

Benefits offered by this job

Medical coverage

Dental coverage

Vision coverage

401K

Generous PTO

Life insurance

Employee assistance

Pet insurance

Job summary

Vericast is seeking a Senior Data Engineer in Texas to build big data pipelines, automate marketing campaigns, and support data needs for analytics, reporting, and AI applications. This role will expand our data infrastructure and pipeline architecture while optimizing data flow into our Data Lakehouse.

The Senior Data Engineer will work with analytics, reporting, and data science teams to ensure a consistent, scalable data delivery architecture across projects.

Qualifications

• 5+ years in Data Engineering or Data Platform Engineering.
• Experience with data lakes, pipelines, and BI/AI data needs.
• Strong Python and PySpark for production pipelines.
• Experience with FI/Banking or marketing data is a plus.
• Knowledge of distributed systems and CI/CD practices.

Responsibilities

• Develop scalable data pipelines and integrations.
• Build a robust Data Lakehouse for marketing solutions and FI clients.
• Collaborate with AI, Analytics, and business teams to improve data models.
• Monitor data quality and ensure production data accuracy.
• Provide post-deployment support and troubleshoot issues.

Skills

Python

PySpark

Airflow

Spark

Hive

Iceberg

S3

Trino

Kubernetes

Linux

REST APIs

Education

Bachelor's degree in Computer Science or related field

Master's degree preferred

Tools

Airflow

Iceberg

Hive

S3

Trino

GitLab

Kubernetes

Ranger

Job description

Vericast is seeking a Senior Data Engineer in Texas to build big data pipelines, automate marketing campaigns, and support data needs for analytics, reporting, and AI applications. This role will expand our data infrastructure and pipeline architecture while optimizing data flow into our Data Lakehouse.

The Senior Data Engineer will work with analytics, reporting, and data science teams to ensure a consistent, scalable data delivery architecture across projects.