Google · google.com · checked today
Staff Data Engineer, Trust and Safety Tech and Data Enablement
The Google Cloud Consulting Professional Services team guides customers through the moments that matter most in their cloud journey to help businesses thrive. We help customers transform and evolve their business through the use of Google’s global network, web-scale data centers, and software infrastructure. As part of an innovative team in this rapidly growing business, you will help shape the future of businesses of all sizes and use technology to connect with customers, employees, and partners.
Skills, with evidence
- Data modelling
Interpreting and designing data elements, structures, and relationships to deliver consistent and predictable data models.
must have - Python
10 years of experience writing code using specialized languages and libraries (e.g., Python, R, SQL) for datasets.
must have - SQL
10 years of experience writing code using specialized languages and libraries (e.g., Python, R, SQL) for datasets.
must have - Spark
10 years of experience with data processing software (e.g., Hadoop, Spark, Pig, Hive) and algorithms (e.g., MapReduce, Flume).
must have - Warehousing
Designing, evaluating, and managing scalable data storage and processing ecosystems (e.g., Data Lakes, Data Warehouses, Data Mesh).
must have - Airflow / orchestration
Designing and implementing complex ETL pipelines for synchronous and asynchronous system integration.
- GCP
The Google Cloud Consulting Professional Services team guides customers through the moments that matter most in their cloud journey to help businesses thrive.
not practised here - Machine learning
Leverage , deploy, and continuously train ML models while implementing new storage and serving systems to scale across Data Engineering.
not practised here
Your plan
- The SQL screen: correct, then fast≈ 2 h
SQL
- Python: the data-wrangling round≈ 4 h
Python
- Data modelling: the round most people fail≈ 4 h
Data modelling · Warehousing
- Pipeline design: safe to run twice≈ 4 h
Airflow / orchestration
- Fourteen months of wrong numbersAdvanced
- You fixed it. Who already used it?Advanced
- The payload changed overnightAdvanced
- Creator payout runAdvanced
- Delete me from everywhereAdvanced
- Spark: read the plan Spark actually ran≈ 2 h
Spark
- applyInPandas per country: what Spark ships to Python, and the native rewriteAdvanced
- Filters you wrote in the wrong place: where Catalyst moves themAdvanced
- snappy, gzip or zstd: measure the Parquet codec trade-off yourselfAdvanced
- A correlated COUNT(*) subquery: the join Spark runs, and the count bug it avoidsAdvanced
- Find the four suspects in a slow pipeline (one of them is innocent)Advanced
- Say it out loud≈ 1 h
Not covered by the plan: GCP, Machine learning.
Readiness
Counted from drills you have completed anywhere on D8LooP.
leaves in 6 dremoved the moment Google closes it