PySpark Developer Trainer

PySpark Developer Trainer

Roles & Responsibilities:

• Deliver engaging, hands-on training sessions on

  PySpark fundamentals and advanced data engineering concepts

  .
• Design and implement

  real-world projects, case studies, and capstone assignments

  to reinforce learning.
• Teach

  RDDs, DataFrames, Datasets, Spark SQL

  , and Spark Streaming with practical use cases.
• Guide learners in

  data ingestion, transformation, and optimization

  using Spark and related tools.
• Support students with code reviews, debugging sessions, and conceptual understanding.
• Evaluate learner progress through

  projects, quizzes, assignments, and live coding challenges

  .
• Conduct

  webinars, live ETL pipelines builds

  , and

  industry-focused Q&A sessions

  .
• Adapt teaching methodology to suit both

  entry-level and advanced learners

  .

Technology-Specific Responsibilities:

Core PySpark & Apache Spark:

• Train students in

  Spark architecture, RDD vs DataFrame vs Dataset

  , and lazy evaluation.
• Teach efficient use of

  Spark transformations, actions, and execution planning

  .
• Demonstrate building

  ETL pipelines

  , handling large-scale data processing with

  Spark SQL and UDFs

  .

Data Engineering & Processing:

• Guide on working with

  structured and semi-structured data (CSV, JSON, Parquet, ORC)

  .
• Teach performance tuning techniques:

  caching, partitioning, broadcast joins

  .
• Introduce

  streaming concepts

  using

  Spark Structured Streaming

  .

Big Data Ecosystem:

• Familiarize learners with tools like

  HDFS, Hive, Kafka, Airflow

  , and

  Delta Lake

  .
• Cover

  integration with data sources (JDBC, S3, NoSQL)

  and

  data lakes

  .

Advanced Tools & Best Practices:

• Train on

  writing modular, testable Spark code

  using Python best practices.
• Demonstrate

  logging, error handling

  , and

  unit testing with PyTest

  .
• Emphasize

  performance, scalability, and cluster resource tuning

  .
• Introduce

  CI/CD, Git-based workflows

  , and cloud deployments (AWS EMR, Databricks, GCP Dataproc).

Requirements:

• 2–5 years of professional experience in

  PySpark and big data development

  .
• Strong understanding of

  Python, Spark internals, and distributed systems

  .
• Proficiency in

  SQL, Spark SQL, and data transformation pipelines

  .
• Experience with

  data modeling, job scheduling, and workflow orchestration

  .
• Ability to simplify technical concepts and mentor aspiring developers effectively.

Preferred Skills:

• Exposure to

  Databricks

  ,

  AWS Glue

  , or

  GCP BigQuery + Dataproc

  .
• Familiarity with

  Apache Airflow

  , Docker, or

  Kubernetes-based data pipelines

  .
• Experience with

  batch vs real-time architectures

  , Spark MLlib, or GraphFrames.
• Knowledge of

  DevOps tools (Git, Jenkins)

  for data workflows.
• Exposure to

  Delta Lake, Iceberg, or Lakehouse architectures

  .

Why Join Us?

• Inspire and guide the next generation of

  data engineers and PySpark developers

  .
• Be part of a

  collaborative, innovative, and flexible

  learning ecosystem.
• Enjoy

  remote work opportunities and flexible teaching schedules

  .
• Competitive pay with additional opportunities in

  curriculum building, content creation

  , and

  community leadership

  .