Data Engineering Product Manager

4 weeks ago


North East, United Kingdom alphanumeric Full time
Job Details

Data Engineering Product Manager

Alphanumeric is seeking a skilled Product Manager to lead the development and execution of our Data Engineering products. The ideal candidate will have a strong understanding of data engineering tools and cloud computing environments, as well as experience working in agile software development environments.

The Product Manager will be responsible for developing and executing the product strategy of our Data Engineering products to meet customer needs. This includes partnering closely with engineering teams to deliver industry-leading Data Engineering products that power downstream consumption of petabytes of business-critical data.

The successful candidate will have a strong analytical and problem-solving skills, with the ability to make data-driven decisions. They will also have experience launching and managing high-stakes enterprise engineering products for bioinformatics, data science, AI/ML, and analytics data consumption.

Job Requirements

EDUCATION/JOB-RELATED EXPERIENCE:

Masters + 2 years

Bachelors + 4 years

At least 2 years of experience in Data Engineering and/or cloud infrastructure product development or management

TECHNICAL KNOWLEDGE:

  1. Proficiency with modern software development tools/ways of working (e.g. git/GitHub, Docker, DevOps tools, metrics/monitoring, CI/CD, etc.).
  2. Strong understanding of data engineering tools (Spark, Airflow, PySpark, Kafka, Hive, SQL/NoSQL...); working with at least one programming language (Python, Java, or Scala).
  3. Familiarity with cloud computing environments, including but not limited to GCP (Preferred), Azure, and AWS.
  4. Experience working in agile software development environments, utilizing tools like Jira and Confluence.
  5. Familiar with metadata management and data governance concepts.
  6. Familiarity with life science/biological data is a plus.

EXPERIENCE:

2+ years of experience in product management, with a focus on data engineering products.

Demonstrated success in launching and managing high-stakes enterprise engineering products for bioinformatics, data science, AI/ML, and analytics data consumption.



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