精选DS岗位 | Datadog、Lyft、Amazon公司岗位发布！
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You have a BS/MS/PhD in a Computer Science, Engineering or related scientific field or equivalent professional experience
Significant backend programming experience in one or more languages (Python, Go, Scala, Java, SQL)
Experience writing data pipelines using a distributed data framework (Spark, Trino, Hive, Snowflake, etc.)
Knowledge of working with workflow management tools (Luigi, Airflow, Temporal, or Dagster, etc.)
Skilled in wrangling huge amounts of data and enjoy exploring new data sets
Someone who values code simplicity and performance
Interested in working in a fast-paced, high growth environment that respects its engineers and customers
Degree in a quantitative field like statistics, economics, applied math, operations research or engineering. Advanced degrees (Master’s or PhD) are preferred but not required 3+ years of industry experience in a product analytics-based science or stats role Proficiency in SQL - able to write structured and efficient queries on large data sets Experience in programming, especially with data science and visualization libraries in Python Experience with Machine Learning at a basic or higher level is desirable Experience in experimentation and statistical analysis Strong oral and written communication skills, and ability to collaborate with cross-functional partners to build the business
https://app.careerpuck.com/job-board/lyft/job/6954581002
【Amazon】
职位：Data Engineer II, MADS Brand Measurement, Brand Measurement, Ad Tech, and Data Science (MADS)
BASIC QUALIFICATIONS
3+ years of data engineering experience
Experience with data modeling, warehousing and building ETL pipelines
Experience with SQL
PREFERRED QUALIFICATIONS
Experience with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, and IAM roles and permissions
Experience with non-relational databases / data stores (object storage, document or key-value stores, graph databases, column-family databases
Experience with Big Data technologies such as Spark (Scala), Hadoop or any other distributed data processing engine
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