Senior Data Engineer

Vericast•San Antonio, TX

14d

About The Position

Vericast is seeking a motivated Senior Data Engineer to build big data pipelines, automate marketing campaigns, and support data requirements for our analytics, reporting, and AI applications. This role will expand our data infrastructure and pipeline architecture while optimizing data flow and collection into our Data Lake environment. The Senior Data Engineer will work closely with our analytics, reporting, and data science teams on data initiatives, ensuring that a consistent, optimal data delivery architecture is available throughout ongoing projects. The ideal candidate is self-directed and comfortable supporting the data needs of multiple teams, systems, and products. This role is a great fit for someone who enjoys working in an agile team environment and contributing to high-quality, data-driven marketing products in close collaboration with developers and business partners.

Requirements

Bachelor's degree in Computer Science, Information Technology, or a related field, with 5+ years of relevant experience; or Master's degree in Computer Science, Information Technology, or a related field ( preferred ).
5+ years of experience in Data Engineering, ETL development, or Data Platform Engineering.
Experience working within a Financial Institution (FI), Banking, FinTech, MarTech, AdTech, Marketing Services, or Marketing Agency environment.
Experience supporting customer, campaign, audience, marketing performance, attribution, advertising, or transactional data at scale.
Strong experience with Python and PySpark for building production-grade data pipelines.
Experience designing, building, and supporting Data Lakehouse environments.
Experience working with Apache Airflow, Iceberg, Hive, S3, and Trino.
Experience with cloud platforms such as AWS, Azure, or GCP.
Experience with Agile software development methodologies.
Experience with GitLab and CI/CD processes.
Experience supporting AI and machine learning applications.
Understanding of machine learning models and the data requirements needed to support Data Science teams.
Strong programming skills in object-oriented/functional scripting languages such as Python and PySpark for building data pipelines, with experience in testing, logging, and ensuring data observability.
Experience with distributed systems and parallel data processing using big data tools such as Spark, PySpark, Hadoop, Kafka, and Hive.
Proficiency with relational databases.
Strong knowledge of Linux/Unix-based systems.
Strong experience with Iceberg, Hive, S3, and Trino.
Experience building data pipelines with orchestration tools such as Apache Airflow.
Hands-on experience with Apache Ranger and Rancher/Kubernetes.
Excellent analytical, conceptual, and problem-solving skills.
Strong communication skills that promote cross-team collaboration.
Experience supporting data requirements for AI applications. ( Preferred )
Understanding of machine learning models and algorithms, with the ability to interface with the Data Science team. ( Preferred )
Experience building REST APIs. ( Preferred )

Nice To Haves

Master's degree in Computer Science, Information Technology, or a related field.
Experience building REST APIs.
Experience supporting AI and machine learning applications.
Understanding of machine learning models and the data requirements needed to support Data Science teams.
Understanding of machine learning models and algorithms, with the ability to interface with the Data Science team.
Experience building REST APIs.

Responsibilities

Develop scalable data pipelines and build new integrations to support continued growth in data volume and complexity.
Build a robust Data Lakehouse that supports Vericast's marketing solutions business and financial institution clients, including pre-sales, campaign execution, and campaign performance analysis and benchmarking.
Collaborate with AI, Analytics, and business teams to improve data models that feed AI and analytics tools, increasing data accessibility and fostering data-driven decision-making across the organization.
Implement processes and systems to monitor data quality and ensure the availability and accuracy of production data for key stakeholders and dependent business processes.
Perform data analysis to troubleshoot data-related issues and support their resolution.
Provide post-deployment support, responding quickly to resolve unexpected production issues.
Partner with business units and engineering teams to shape long-term data platform architecture strategy.