Job description

  • Responsible for building and maintaining the machine learning data and development platform.
  • Build, integrate and deploy machine learning solutions into the BlackLine application in collaboration with product management, cloud, engineering and data science teams.
  • Create and maintain scalable data pipeline in the cloud (AWS and GCP).
  • Assemble large, complex data sets that meet functional / non-functional business requirements.
  • Identify, design, and implement processes automation and data delivery.
  • Build infrastructure for optimal extraction, transformation, and loading of data from a wide variety of data sources.
  • Execute extract, transform and load (ETL) operations on large datasets including data identification, mapping, aggregation, conditioning, cleansing, and analyzing.
  • Build analytics tools to provide actionable insights into business and product performance.
  • Keep data separated, isolated and secured.
  • Assist data scientists in implementing achine learning algorithms and contribute to building and optimizing our product into an innovative industry leader.
  • Participate in establishing best practices while team is transitioning to new technologies, tools and infrastructure. Maintain specifications and metadata; follow the best practices.
  • Recommend and implement process improvements.
  • Maintain specifications and metadata; follow and develop best practices.
  • Coach and technically train data analysts, if needed.
  • 5+ years as a data engineer.
  • Experience with SQL, Python, R languages.
  • ETL experience using Python.
  • Experience with Hadoop, Spark, Hive. Presto is a plus.
  • Practical experience with GIT version control.
  • Strong familiarity with GCP, AWS, SQL Server.
  • Comfortable working with open source tools in Unix/Linux environments.
  • Data warehousing experience, data modeling and database design.
  • Experience with machine learning packages and various ML algorithms.
  • Experience with predictive and prescriptive analytics, modeling, and segmentation.
  • Experience with data analytics, big data, and analytics architectures.
  • Comfortable handling large amounts of data.
  • Experience ensuring data and modeling accuracy, cleanliness, reliability.
  • Works independently without the need for supervision.
  • Experience translating business requirements into functional, and non-functional requirements.
  • Strong sense systems and data ownership.

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