Adobe · adobe.wd5.myworkdayjobs.com · checked today
Data Platform Engineer
We are hiring a Data Platform Engineer to manage and improve Adobe's enterprise data and AI platform. In this position, you will work closely with engineering, analytics, and business units to deliver consistent, secure, and scalable data services that support reporting, machine learning, and innovative AI projects. This role allows you to engage with modern cloud tools and help teams gain value from extensive data sets.
Skills, with evidence
- AWS
3+ years of hands-on experience in Azure/AWS Cloud Infrastructure, supporting enterprise-scale cloud data platforms.
must have · not practised here - Azure
3+ years of hands-on experience in Azure/AWS Cloud Infrastructure, supporting enterprise-scale cloud data platforms.
must have · not practised here - Python
Experience developing scripts or automation using Python, Bash, or similar technologies.
must have - SQL
Previous SQL Server Administration experience to provide operational support during the transition to new platforms.
- Spark
Coordinate and support enterprise Databricks environments across Azure and AWS.
- Airflow / orchestration
Experience with Apache Airflow, Kubernetes (AKS or EKS), or Apache Kafka.
- Warehousing
Handle cloud-based data storage systems like Azure Data Lake Storage (ADLS Gen2) and Amazon S3.
- Dashboards & BI
Support analytics and visualization platforms including Power BI and Tableau.
not practised here - Kubernetes
Experience with Apache Airflow, Kubernetes (AKS or EKS), or Apache Kafka.
not practised here - Cost & performance
Find opportunities to optimize cloud spending and improve resource utilization.
- Data quality
Experience with monitoring and observability platforms such as Databricks System Tables, Splunk, or Prometheus.
- Failure handling
Experience with monitoring and observability platforms such as Databricks System Tables, Splunk, or Prometheus.
- Streaming
Experience with Apache Airflow, Kubernetes (AKS or EKS), or Apache Kafka.
- Governance & security
Partner with security teams to implement governance, compliance, and data protection controls.
not practised here
Your plan
- The SQL screen: correct, then fast≈ 1 h
SQL · Data quality
- Average order value by countryFoundations
- Line items per orderFoundations
- Revenue by buyer countryFoundations
- Customers who never orderedFoundations
- Hiring funnel by roleFoundations
- Python: the data-wrangling round≈ 2 h
Python
- Clean spreadsheet headers into column namesFoundations
- Events normalization jobFoundations
- List the distinct composite keysFoundations
- Deduplicate rows, first one winsFoundations
- Render a byte count for humansFoundations
- Data modelling: the round most people fail≈ 2 h
Warehousing
- Marketplace core entitiesFoundations
- Cinema seat bookingFoundations
- City parking baysFoundations
- Dating app matchesFoundations
- Ride hailing tripsFoundations
- Pipeline design: safe to run twice≈ 3 h
Airflow / orchestration · Failure handling · Streaming
- After the first one finishesFoundations
- Run it for last TuesdayFoundations
- The history nobody keptFoundations
- The spreadsheet is a dependencyFoundations
- Too slow by morningFoundations
- Spark: read the plan Spark actually ran≈ 1 h
Spark · Cost & performance
- HAVING vs WHERE: where does a filter after groupBy actually run?Foundations
- countDistinct vs approx_count_distinct: what the extra shuffle buysFoundations
- COUNT(*) vs COUNT(column): the null trapFoundations
- Grouping by two columns: what changes in the shuffle?Foundations
- Does the join type change the join strategy?Foundations
- Say it out loud≈ 1 h
Not covered by the plan: AWS, Azure, Dashboards & BI, Kubernetes, Governance & security.
Readiness
Counted from drills you have completed anywhere on D8LooP.
leaves in 9 dremoved the moment Adobe closes it