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Tech Stack
Tools & technologiesAirflowApacheAWSAzureCloudDistributed SystemsETLGoogle Cloud PlatformHadoopKafkaKubernetesLinuxPySparkPythonSparkUnix
About the role
Key responsibilities & impact- Develop scalable data pipelines and build new integrations to support continued growth in data volume and complexity
- Build a robust Data Lakehouse supporting Vericast's marketing solutions business and financial institution clients
- Support pre-sales, campaign execution, campaign performance analysis, and benchmarking
- Collaborate with AI, Analytics, and business teams to improve data models feeding AI and analytics tools
- Implement processes and systems to monitor data quality and ensure production data availability and accuracy
- Perform data analysis to troubleshoot data-related issues and support resolution
- Provide post-deployment support and resolve unexpected production issues
- Partner with business units and engineering teams to shape long-term data platform architecture strategy
Requirements
What you’ll need- 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
- Preferred experience building REST APIs
- Strong programming skills in Python and PySpark, including testing, logging, and data observability
- Experience with distributed systems and parallel data processing using Spark, PySpark, Hadoop, Kafka, and Hive
- Proficiency with relational databases
- Strong knowledge of Linux/Unix-based systems
- Hands-on experience with Apache Ranger and Rancher/Kubernetes
- Excellent analytical, conceptual, and problem-solving skills
- Strong communication skills that promote cross-team collaboration
Benefits
Comp & perks- Medical coverage
- Dental coverage
- Vision coverage
- 401K
- Generous PTO allowance
- Life insurance
- Employee assistance
- Pet insurance