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- Develops and maintain scalable data pipelines
- Build out new API integrations to support continuing increases in data volume and complexity.
- Work on Apache NiFi an ETL tool with flow-based programming that comes with a web UI built to provide an easy way (drag & drop) to handle data flow in real-time. This also supports powerful and scalable means of data routing and transformation, which can be run on a single server or in a clustered mode across many servers.
- builds data processing frameworks that handle the business’s growing database.
- Collaborates with analytics and business teams to improve data models that feed business intelligence tools increasing data accessibility and fostering data-driven decision making across the organization.
- Implements processes and systems to monitor data quality, ensuring production data is always accurate and available for key stakeholders and business processes that depend on it.
- Writes unit/integration tests, contributes to engineering wiki, and documents work.
- Performs data analysis required to troubleshoot data related issues and assist in the resolution of data issues.
- Works closely with a team of frontend and backend engineers, product managers, and analysts.
- Defines company data assets (data models), spark, sparkSQL, and hiveSQL jobs to populate data models.
- Designs data integrations and data quality framework.
- Designs and evaluates open source and vendor tools for data lineage.
- Works closely with all business units and engineering teams to develop strategy for long term data platform architecture.
- Experience with or knowledge of Agile methodologies
- Excellent problem solving and troubleshooting skills
- Process oriented with great documentation skills
- Excellent oral and written communication skills with a keen sense of customer service
- Must have had at least 5 years of working experience working in a data engineering department, preferably as a Data Engineer in a fast-paced environment and complex business setting. Experience working with varied forms of data infrastructure inclusive of relational databases such as SQL, Hadoop, Spark, NIFI and column-oriented databases such as Redshift, MySQL, and Vertica.