PySpark Developer Trainer (Big Data, Spark, DataFrames, ETL, SQL, Python)

Job Description:

We are seeking a knowledgeable and passionate PySpark Developer Trainer with deep expertise in Apache Spark, Python, and big data technologies. The ideal candidate will have 2–5 years of hands-on experience in PySpark development and a genuine interest in teaching and mentoring aspiring data engineers and analysts.

Roles & Responsibilities:

• Deliver engaging, hands-on training sessions on PySpark fundamentals and advanced data engineering concepts.
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• Design and implement real-world projects, case studies, and capstone assignments to reinforce learning.
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• Teach RDDs, DataFrames, Datasets, Spark SQL, and Spark Streaming with practical use cases.
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• Guide learners in data ingestion, transformation, and optimization using Spark and related tools.
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• Support students with code reviews, debugging sessions, and conceptual understanding.
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• Evaluate learner progress through projects, quizzes, assignments, and live coding challenges.
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• Conduct webinars, live ETL pipelines builds, and industry-focused Q&A sessions.
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• Adapt teaching methodology to suit both entry-level and advanced learners.
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Technology-Specific Responsibilities:

Core PySpark & Apache Spark:

• Train students in Spark architecture, RDD vs DataFrame vs Dataset, and lazy evaluation.
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• Teach efficient use of Spark transformations, actions, and execution planning.
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• Demonstrate building ETL pipelines, handling large-scale data processing with Spark SQL and UDFs.
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Data Engineering & Processing:

• Guide on working with structured and semi-structured data (CSV, JSON, Parquet, ORC).
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• Teach performance tuning techniques: caching, partitioning, broadcast joins.
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• Introduce streaming concepts using Spark Structured Streaming.
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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:

• 1–5 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.