A Data Engineer at Google, particularly within Google Cloud, is responsible for designing, building, and maintaining systems for data ingestion, storage, processing, and analysis. They ensure data pipelines are reliable and efficient, implement data governance and security best practices, and work with various teams to understand business needs and translate them into technical solutions.
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IT Includes
1.1 Designing flexible data representations. Considerations include:
future advances in data technology
changes to business requirements
awareness of current state and how to migrate the design to a future state
data modeling
tradeoffs
distributed systems
schema design
1.2 Designing data pipelines. Considerations include:
future advances in data technology
changes to business requirements
awareness of current state and how to migrate the design to a future state
data modeling
tradeoffs
system availability
distributed systems
schema design
common sources of error (eg. removing selection bias)
1.3 Designing data processing infrastructure. Considerations include:
future advances in data technology
changes to business requirements
awareness of current state, how to migrate the design to the future state
data modeling
tradeoffs
system availability
distributed systems
schema design
capacity planning
different types of architectures: message brokers, message queues, middleware, service-oriented2.1 Building and maintaining flexible data representations
2.2 Building and maintaining pipelines. Considerations include:
data cleansing
batch and streaming
transformation
acquire and import data
testing and quality control
connecting to new data sources
2.3 Building and maintaining processing infrastructure. Considerations include:
provisioning resources
monitoring pipelines
adjusting pipelines
testing and quality control3.1 Analyzing data. Considerations include:
data collection and labeling
data visualization
dimensionality reduction
data cleaning/normalization
defining success metrics
3.2 Machine learning. Considerations include:
feature selection/engineering
algorithm selection
debugging a model
3.3 Machine learning model deployment. Considerations include:
performance/cost optimization
online/dynamic learning4.1 Mapping business requirements to data representations. Considerations include:
working with business users
gathering business requirements
4.2 Optimizing data representations, data infrastructure performance and cost. Considerations include:
resizing and scaling resources
data cleansing, distributed systems
high performance algorithms
common sources of error (eg. removing selection bias)5.1 Performing quality control. Considerations include:
verification
building and running test suites
pipeline monitoring
5.2 Assessing, troubleshooting, and improving data representations and data processing infrastructure.
5.3 Recovering data. Considerations include:
planning (e.g. fault-tolerance)
executing (e.g., rerunning failed jobs, performing retrospective re-analysis)
stress testing data recovery plans and processes6.1 Building (or selecting) data visualization and reporting tools. Considerations include:
automation
decision support
data summarization, (e.g, translation up the chain, fidelity, trackability, integrity)
6.2 Advocating policies and publishing data and reports.7.1 Designing secure data infrastructure and processes. Considerations include:
Identify and Access Management (IAM)
data security
penetration testing
Separation of Duties (SoD)
security control
7.2 Designing for legal compliance. Considerations include:
legislation (e.g., Health Insurance Portability and Accountability Act (HIPAA), Children’s Online Privacy Protection Act (COPPA), etc.)
audits
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