Data Engineer - 12565
Pleasanton, United States
- 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.