Skill Profile

Apache Spark

PySpark, Spark SQL, DataFrames, partitioning, optimization, Spark on K8s

Data Engineering Batch Processing

Roles

where this skill appears

Levels

structured growth path

Mandatory requirements

the other 2 optional

Data Engineering

Batch Processing

3/17/2026

Choose your current level and compare expectations. The items below show what to cover to advance to the next level.

What is Expected at Each Level

The table shows how skill depth grows from Junior to Principal. Click a row to see details.

Role	Required	Description
Data Engineer	Required	Understands Apache Spark fundamentals for data engineering: RDD/DataFrame APIs, basic transformations and actions, reading/writing Parquet/CSV/JSON. Follows team patterns for PySpark job structure, SparkSession configuration, and cluster resource allocation.
Data Scientist		Understands Apache Spark fundamentals for data science: Spark DataFrames for large-scale data analysis, basic Spark SQL queries, and MLlib for distributed model training. Follows team patterns for notebook-based Spark workflows and feature engineering at scale.
ML Engineer	Required	Understands Apache Spark fundamentals for ML engineering: Spark MLlib pipelines, feature transformers, and distributed model training/inference. Follows team patterns for PySpark ML workflows, model serialization, and integration with MLflow tracking.

Role	Required	Description
Data Engineer	Required	Independently implements Spark data pipelines: optimizes shuffle operations and partitioning strategies, implements Structured Streaming for real-time ETL, manages Delta Lake tables with ACID transactions. Tunes Spark configurations for memory, parallelism, and cost efficiency.
Data Scientist		Independently uses Spark for large-scale analysis: writes optimized Spark SQL for complex aggregations, implements distributed feature engineering with window functions, and uses MLlib for hyperparameter tuning at scale. Manages Spark resource allocation for interactive analytics.
ML Engineer	Required	Uses PySpark for large-scale feature engineering. Optimizes Spark jobs (partitioning, caching, broadcast joins). Uses Spark ML for distributed model training.

Role	Required	Description
Data Engineer	Required	Designs Spark-based data platform architecture: multi-tenant cluster management, cost-optimized workload scheduling with YARN/Kubernetes, and lakehouse architecture with Delta Lake/Iceberg. Implements data quality frameworks, CDC pipelines, and Spark application performance monitoring.
Data Scientist	Required	Designs Spark-based analytical frameworks: custom MLlib transformers for domain-specific features, distributed experiment pipelines, and Spark integration with GPU-accelerated training (Rapids). Optimizes end-to-end ML workflows from data preparation to model serving at petabyte scale.
ML Engineer	Required	Designs Spark-based ML pipelines for production. Optimizes Spark for ML workloads: memory tuning, shuffle optimization. Integrates Spark with ML platform (MLflow, feature store).

Role	Required	Description
Data Engineer	Required	Defines Spark standards: coding guidelines, job submission patterns, resource allocation policies. Chooses between PySpark and Spark SQL by scenario. Implements unit testing for Spark jobs through chispa.
Data Scientist	Required	Defines data engineering strategy. Shapes data platform. Coordinates data teams. Optimizes data mesh/data fabric approaches.
ML Engineer	Required	Defines Spark strategy for ML data processing. Evaluates Spark vs alternatives (Dask, Ray) for ML workloads. Designs distributed computing architecture for ML.

Role	Required	Description
Data Engineer	Required	Designs platform Spark strategy: EMR vs Databricks vs self-hosted, cluster sizing, dynamic allocation. Defines when Spark vs DuckDB vs Polars. Plans migration to Spark 4.0.
Data Scientist	Required	Defines organizational data strategy. Designs enterprise data platform. Establishes data governance framework.
ML Engineer	Required	Defines distributed processing strategy for enterprise ML. Designs data processing layer for ML platform. Evaluates novel distributed frameworks.

Junior 3 requirements

Data Engineer
Required

Understands Apache Spark fundamentals for data engineering: RDD/DataFrame APIs, basic transformations and actions, reading/writing Parquet/CSV/JSON. Follows team patterns for PySpark job structure, SparkSession configuration, and cluster resource allocation.
Data Scientist

Understands Apache Spark fundamentals for data science: Spark DataFrames for large-scale data analysis, basic Spark SQL queries, and MLlib for distributed model training. Follows team patterns for notebook-based Spark workflows and feature engineering at scale.
ML Engineer
Required

Understands Apache Spark fundamentals for ML engineering: Spark MLlib pipelines, feature transformers, and distributed model training/inference. Follows team patterns for PySpark ML workflows, model serialization, and integration with MLflow tracking.

Middle 3 requirements

Data Engineer
Required

Independently implements Spark data pipelines: optimizes shuffle operations and partitioning strategies, implements Structured Streaming for real-time ETL, manages Delta Lake tables with ACID transactions. Tunes Spark configurations for memory, parallelism, and cost efficiency.
Data Scientist

Independently uses Spark for large-scale analysis: writes optimized Spark SQL for complex aggregations, implements distributed feature engineering with window functions, and uses MLlib for hyperparameter tuning at scale. Manages Spark resource allocation for interactive analytics.
ML Engineer
Required

Uses PySpark for large-scale feature engineering. Optimizes Spark jobs (partitioning, caching, broadcast joins). Uses Spark ML for distributed model training.

Senior 3 requirements

Data Engineer
Required

Designs Spark-based data platform architecture: multi-tenant cluster management, cost-optimized workload scheduling with YARN/Kubernetes, and lakehouse architecture with Delta Lake/Iceberg. Implements data quality frameworks, CDC pipelines, and Spark application performance monitoring.
Data Scientist
Required

Designs Spark-based analytical frameworks: custom MLlib transformers for domain-specific features, distributed experiment pipelines, and Spark integration with GPU-accelerated training (Rapids). Optimizes end-to-end ML workflows from data preparation to model serving at petabyte scale.
ML Engineer
Required

Designs Spark-based ML pipelines for production. Optimizes Spark for ML workloads: memory tuning, shuffle optimization. Integrates Spark with ML platform (MLflow, feature store).

Lead / Staff 3 requirements

Data Engineer
Required

Defines Spark standards: coding guidelines, job submission patterns, resource allocation policies. Chooses between PySpark and Spark SQL by scenario. Implements unit testing for Spark jobs through chispa.
Data Scientist
Required

Defines data engineering strategy. Shapes data platform. Coordinates data teams. Optimizes data mesh/data fabric approaches.
ML Engineer
Required

Defines Spark strategy for ML data processing. Evaluates Spark vs alternatives (Dask, Ray) for ML workloads. Designs distributed computing architecture for ML.

Principal 3 requirements

Data Engineer
Required

Designs platform Spark strategy: EMR vs Databricks vs self-hosted, cluster sizing, dynamic allocation. Defines when Spark vs DuckDB vs Polars. Plans migration to Spark 4.0.
Data Scientist
Required

Defines organizational data strategy. Designs enterprise data platform. Establishes data governance framework.
ML Engineer
Required

Defines distributed processing strategy for enterprise ML. Designs data processing layer for ML platform. Evaluates novel distributed frameworks.

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