NVIDIA · nvidia.wd5.myworkdayjobs.com · checked today
Senior Analytics Engineer, Finance
NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s a unique legacy of innovation that’s fueled by great technology—and amazing people. Today, we’re tapping into the unlimited potential of AI to define the next era of computing.
Skills, with evidence
- Data modelling
Strong SQL skills and proficiency in data modeling concepts (star schema, dimensional modeling, semantic design).
must have - Data quality
Collaborate with data engineering teams to optimize data pipelines, ensure data quality, and improve data availability.
must have - Incremental loads
Practical involvement with traditional data warehouse architectures, including centralized data warehouses and/or data marts, and a solid understanding of historical, snapshot, and incremental data processing patterns.
must have - SQL
Strong SQL skills and proficiency in data modeling concepts (star schema, dimensional modeling, semantic design).
must have - Governance & security
Establish and implement data governance, validation rules, and transformation logic to ensure data consistency across systems including Unity Catalog policies within Databricks.
must have · not practised here - Spark
We are seeking a highly skilled Senior Analytics Engineer supporting finance initiatives, with strong end-to-end analytics capabilities, spanning data engineering fundamentals, semantic layer design, dashboard development in Tableau and Power BI, and application development within Databricks.
- Warehousing
The ideal candidate will bring strong Finance domain knowledge, experience improving reporting and business processes, hands-on expertise building semantic layers to develop consistent, well-governed analytical data models, and proficiency designing and deploying data applications on the Databricks Lakehouse Platform.
- Dashboards & BI
We are seeking a highly skilled Senior Analytics Engineer supporting finance initiatives, with strong end-to-end analytics capabilities, spanning data engineering fundamentals, semantic layer design, dashboard development in Tableau and Power BI, and application development within Databricks.
not practised here - Airflow / orchestration
Background with ETL/ELT tools (e.g., dbt, Fivetran, Informatica, Data Factory, Alteryx) and project tracking in Jira.
- Cost & performance
Collaborate with data engineering teams to optimize data pipelines, ensure data quality, and improve data availability.
- Failure handling
Supervise and support interface health, error handling, and reconciliation processes, proactively resolving integration failures and data discrepancies.
- Python
Exposure to data science tools or methodologies (Python, R, machine learning concepts, Snowflake).
- dbt
Background with ETL/ELT tools (e.g., dbt, Fivetran, Informatica, Data Factory, Alteryx) and project tracking in Jira.
- Machine learning
Exposure to data science tools or methodologies (Python, R, machine learning concepts, Snowflake).
not practised here
Your plan
- The SQL screen: correct, then fast≈ 2 h
Data quality · SQL
- Median delivery time per cityIntermediate
- Bucket deliveries into quartilesIntermediate
- Median order value without a median functionIntermediate
- New and repeat orders by monthIntermediate
- Every order against its customer's averageIntermediate
- Python: the data-wrangling round≈ 3 h
Python
- Diff two snapshots of a tableIntermediate
- Explode an array column into rowsIntermediate
- Flatten nested event payloadsIntermediate
- Keep only the latest row for each eventIntermediate
- Pivot a long metrics table to wideIntermediate
- Data modelling: the round most people fail≈ 3 h
Data modelling · Warehousing · dbt
- Addresses that stay true to the pastIntermediate
- Seat holds and the release-night raceIntermediate
- Subscription warehouse grainIntermediate
- Campaign efficiencyIntermediate
- Catalogue: products, variants and sellersIntermediate
- Pipeline design: safe to run twice≈ 4 h
Incremental loads · Airflow / orchestration · Failure handling
- Is this change safe?Intermediate
- Marketplace transactions at scaleIntermediate
- The source will not let youIntermediate
- Changing a pipeline that’s already runningIntermediate
- SLA-aware alerting flowIntermediate
- Spark: read the plan Spark actually ran≈ 2 h
Spark · Cost & performance
- broadcast() with auto-broadcast off, and the case where Spark ignores itIntermediate
- autoBroadcastJoinThreshold compares an estimate: flip a join with select() and one settingIntermediate
- Does Spark really run your EXISTS subquery once per row?Intermediate
- left_semi and left_anti: "customers who did / never did" without a full joinIntermediate
- A self-join on a real key that still multiplies rowsIntermediate
- Say it out loud≈ 1 h
25 drills · Intermediate + Advanced≈ 14 hours
Not covered by the plan: Governance & security, Dashboards & BI, Machine learning.
Readiness
Counted from drills you have completed anywhere on D8LooP.
leaves in 4 dremoved the moment NVIDIA closes it