lucas.ai | firstname.lastname@example.org | LinkedIn | GitHub | (386) 315-3717
Looking for Data/Cloud engineering or Machine Learning engineering position. Mid-term goal within 3 year:
- Proficiently apply cloud technology to capture, store, ETL and analyze data at scale.
- Apply machine learning to create useful products.
Data Team Lead @18birdies (Apr/17 - present, Oakland, CA)
- Building a team, infrastructure and data culture from scratch.
- Data service APIs.
- Scalable, fault tolerant and easily orchestrated data pipeline with Spark and Airflow.
Data Engineer @18birdies (Feb/17 - Apr/17, Oakland, CA)
- Build pipeline with Apache Beam and data warehouse with BigQuery, which powers internal dashboards and machine learning models.
- Build pipeline collecting user behavior data and produce analytical reports.
- Build marketing tool for targeted campaigns, AB testing and track the efficacy.
Data Engineer @Sqor (Aug/15 - Feb/17, San Francisco, CA)
- Wrangle, analyze, visualize data. Data dashboards are used by executives everyday.
- Create event delivery pipeline which collects user behavior data using AWS and google cloud.
- Introduce serverless architecture by applying AWS lambda and Google App Engine to create services with bare minimum devop overhead.
- Create search service for user to find friends and hashtags using Elasticsearch and Algolia search.
- Analyze social graph data using graphical database: Neo4j, Cypher.
- numpy, pandas, matplotlib, seaborn, scikit-learn, TensorFlow
- Experience with Scala, R, SQL
- Google Cloud: Dataflow, BigQuery, Pub/Sub, CloudML, Cloud Function, App Engine
- AWS: Lambda, API Gateway, Redshift, Kinesis, DynamoDB
- Open Source: Apache Airflow, Apache Spark
- Implement all exercises with numpy, scipy, sklearn and seaborn. (github)
- Image classification and computer vision with CNN
Master of Computer Science & Engineering, Santa Clara University (2014, Santa Clara, CA)
- Manage to graduate 2-year program in 1 year.