Experienced Data Engineer for Machine Learning Pipeline and Productization
Budget: $750 – $1,500 USD
Join an experienced data science team, working with Deep Learning and advanced bayesian modelling approaches.
It's a super interesting project with challenging research and productisation aspects, major US client (fortune 500), flexible working hours.
Looking for an experienced data engineer and analyst to join our team supporting data querying and analysis towards answering prediction business questions.
Responsibilities:
Dive into enterprise level documentation about data to deeply understand complex columnar database architecture, meaning and table mapping.
Write SQL queries to load data.
Communicate effectively and proactively both internally and externally with enterprise level engineers and executives (client side) to deeply understand and supervise complex databases framework and context.
Advantage: Write scripts to consume or APIs from external systems, pre process and store data in efficient formats.
Must have requirements:
Experience with large-scale data infrastructures, including columnar data stores.
Hands on experience in creating efficient and complex SQL queries of large scale data sets. Preferably against Presto.
Analyst mentality. Get excited about understanding the meaning, mapping the tables, and loading data from complex databases to simplify our machine learning engine processes.
Communication: Fluent in English. Be able to articulate requirements and make proactive recommendations internally with the data science team and externally with client teams.
Experience deploying code to production environments regularly.
Comfortable with *nix, git.
Advantage:
Command of Python. Experience working against AWS services from Python code, writing custom data loaders in the context of Pytorch / TensorFlow / equivalent, working with gRPC in Python.
Experience with AIStore or similar.
AWS. Experienced with setting data pipelines within AWS toolset.
Automation: Experience writing code to automate ETL processes. Preferably using Airflow.
Experience building and deploying Docker containers.
Admin:
Data science team is located partly in CET time zone, and partly in EST time zone. We are also facing the US client on a limited basis on EST time zone. It would be preferred to work and be available around the same hours on a flexible basis.
It's a super interesting project with challenging research and productisation aspects, major US client (fortune 500), flexible working hours.
Looking for an experienced data engineer and analyst to join our team supporting data querying and analysis towards answering prediction business questions.
Responsibilities:
Dive into enterprise level documentation about data to deeply understand complex columnar database architecture, meaning and table mapping.
Write SQL queries to load data.
Communicate effectively and proactively both internally and externally with enterprise level engineers and executives (client side) to deeply understand and supervise complex databases framework and context.
Advantage: Write scripts to consume or APIs from external systems, pre process and store data in efficient formats.
Must have requirements:
Experience with large-scale data infrastructures, including columnar data stores.
Hands on experience in creating efficient and complex SQL queries of large scale data sets. Preferably against Presto.
Analyst mentality. Get excited about understanding the meaning, mapping the tables, and loading data from complex databases to simplify our machine learning engine processes.
Communication: Fluent in English. Be able to articulate requirements and make proactive recommendations internally with the data science team and externally with client teams.
Experience deploying code to production environments regularly.
Comfortable with *nix, git.
Advantage:
Command of Python. Experience working against AWS services from Python code, writing custom data loaders in the context of Pytorch / TensorFlow / equivalent, working with gRPC in Python.
Experience with AIStore or similar.
AWS. Experienced with setting data pipelines within AWS toolset.
Automation: Experience writing code to automate ETL processes. Preferably using Airflow.
Experience building and deploying Docker containers.
Admin:
Data science team is located partly in CET time zone, and partly in EST time zone. We are also facing the US client on a limited basis on EST time zone. It would be preferred to work and be available around the same hours on a flexible basis.