Perfect 👍
Below is a PRODUCTION-GRADE AWS Step Functions JSON that orchestrates a Spark job on EMR Serverless, with wait, retry, failure handling, and SNS alerts.

This is exactly what senior data engineers use.

🧠 CONTEXT (VERY IMPORTANT – READ FIRST)

This Step Function will:

Start
 → Run EMR Serverless Spark job (SYNC)
 → If SUCCESS → Publish success SNS
 → If FAILURE → Retry → Publish failure SNS
 → End

📌 Key concept
We use .sync so Step Functions waits for Spark to finish
(not fire-and-forget like Lambda).

🧩 STEP FUNCTIONS — EMR SERVERLESS SPARK (JSON)

🔹 You can paste this directly into AWS Step Functions → Create State Machine → Code view

{
  "Comment": "Serverless Spark ETL using EMR Serverless with retries and notifications",
  "StartAt": "RunSparkJob",
  "States": {
    "RunSparkJob": {
      "Type": "Task",
      "Resource": "arn:aws:states:::emr-serverless:startJobRun.sync",
      "Parameters": {
        "ApplicationId": "00f2abcd1234example",
        "ExecutionRoleArn": "arn:aws:iam::123456789012:role/EMRServerlessExecutionRole",
        "JobDriver": {
          "SparkSubmit": {
            "EntryPoint": "s3://rajeev-data-lake-2026/scripts/sales_etl.py",
            "EntryPointArguments": [
              "--input",
              "s3://rajeev-data-lake-2026/raw/sales/",
              "--output",
              "s3://rajeev-data-lake-2026/curated/sales/"
            ],
            "SparkSubmitParameters": "--conf spark.executor.memory=4g --conf spark.executor.cores=2"
          }
        }
      },
      "Retry": [
        {
          "ErrorEquals": ["States.ALL"],
          "IntervalSeconds": 60,
          "MaxAttempts": 3,
          "BackoffRate": 2.0
        }
      ],
      "Catch": [
        {
          "ErrorEquals": ["States.ALL"],
          "Next": "NotifyFailure"
        }
      ],
      "Next": "NotifySuccess"
    },

    "NotifySuccess": {
      "Type": "Task",
      "Resource": "arn:aws:states:::sns:publish",
      "Parameters": {
        "TopicArn": "arn:aws:sns:ap-south-1:123456789012:etl-success",
        "Message": "Spark ETL completed successfully using EMR Serverless"
      },
      "End": true
    },

    "NotifyFailure": {
      "Type": "Task",
      "Resource": "arn:aws:states:::sns:publish",
      "Parameters": {
        "TopicArn": "arn:aws:sns:ap-south-1:123456789012:etl-failure",
        "Message": "Spark ETL failed after retries. Please check CloudWatch logs."
      },
      "End": true
    }
  }
}

🔍 DEEP EXPLANATION (LINE-BY-LINE)

🔹 .sync (MOST IMPORTANT)

"Resource": "arn:aws:states:::emr-serverless:startJobRun.sync"

✔ Step Functions waits until Spark finishes
✔ Gets SUCCESS / FAILURE
✔ Enables retry & catch

📌 Without .sync → NO orchestration

🔹 Spark Job Definition

"EntryPoint": "s3://.../sales_etl.py"

✔ Spark code lives in S3
✔ Same script usable by:

• EMR
• EMR Serverless
• CI/CD

🔹 Retry Strategy (Senior-Level)

"Retry": [{
  "IntervalSeconds": 60,
  "MaxAttempts": 3,
  "BackoffRate": 2.0
}]

Meaning:

• Retry after 1 min
• Retry up to 3 times
• Exponential backoff

📌 This replaces custom retry logic

🔹 Failure Handling

"Catch": [{
  "Next": "NotifyFailure"
}]

✔ Centralized failure handling
✔ No silent job failures
✔ Mandatory in real pipelines

🔹 SNS Alerts

"arn:aws:states:::sns:publish"

✔ Email / Slack / PagerDuty
✔ SLA-friendly
✔ Audit-ready

🧠 WHY THIS DESIGN IS INDUSTRY-PERFECT

💬 INTERVIEW-PERFECT ANSWER (USE THIS)

“We orchestrated EMR Serverless Spark jobs using AWS Step Functions with synchronous execution, built-in retries, and SNS-based alerting. This allowed us to run fault-tolerant, cost-optimized Spark pipelines without managing clusters.”

🔥 This answer = senior data engineer signal

🧾 RESUME BULLET (COPY-PASTE READY)

Orchestrated serverless PySpark pipelines using AWS Step Functions with EMR Serverless, implementing retries, failure handling, and SNS alerts

🚀 WHAT NEXT (VERY STRONG OPTIONS)

I can now:
1️⃣ Write the actual PySpark sales_etl.py
2️⃣ Write Lambda trigger code for Step Functions
3️⃣ Create GitHub repo structure (industry-style)
4️⃣ Convert this into diagram + interview Q&A

Tell me 👉 what you want next