Senior Data Engineer

This is a fully remote position, open to applicants in Texas.

📋 Description

• Design and implement scalable data pipelines and create new integrations to accommodate ongoing increases in data volume and complexity.

• Establish a comprehensive Data Lakehouse to support Vericast's marketing solutions and financial institution clients.

• Assist in pre-sales activities, campaign execution, performance analysis, and benchmarking.

• Collaborate with AI, analytics, and business teams to enhance data models that feed AI and analytics tools.

• Develop processes and systems to monitor data quality, ensuring the availability and accuracy of production data.

• Conduct data analysis to diagnose and resolve data-related issues.

• Offer post-deployment support and swiftly address any unexpected production challenges.

• Work in partnership with business units and engineering teams to define the long-term architecture strategy for the data platform.

⛳️ Requirements

• A bachelor's degree in Computer Science, Information Technology, or a related field with over 5 years of relevant experience; a master's degree in Computer Science, Information Technology, or a related field is preferred.

• More than 5 years of experience in Data Engineering, ETL development, or Data Platform Engineering.

• Experience in Financial Institutions (FI), Banking, FinTech, MarTech, AdTech, Marketing Services, or Marketing Agency settings.

• Proven experience supporting customer, campaign, audience, marketing performance, attribution, advertising, or large-scale transactional data.

• Extensive expertise in Python and PySpark for developing production-quality data pipelines.

• Experience in designing, building, and maintaining Data Lakehouse environments.

• Familiarity with Apache Airflow, Iceberg, Hive, S3, and Trino.

• Proficient with cloud platforms such as AWS, Azure, or GCP.

• Experience with Agile software development methodologies.

• Knowledge of GitLab and CI/CD processes.

• Background in supporting AI and machine learning applications.

• Understanding of machine learning models and the data requirements of Data Science teams.

• Experience with distributed systems and parallel data processing using Spark, PySpark, Hadoop, Kafka, and Hive.

• Proficient in relational databases.

• Strong understanding of Linux/Unix-based systems.

• Practical experience with Apache Ranger and Rancher/Kubernetes.

• Excellent analytical, conceptual, and problem-solving abilities.

• Strong communication skills that facilitate collaboration across teams.

🏝️ Benefits

• Medical coverage

• Dental coverage

• Vision coverage

• 401K

• Generous PTO allowance

• Life insurance

• Employee assistance

• Pet insurance