Essential Data Engineering Skills for High-Paying Roles at Meesho or Flipkart

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Data Engineers aiming for top product-based companies (PBCs) like Meesho or Flipkart need a strong foundation in key technologies. Below are the critical skills required to secure a 35+ LPA role:

1. Strong SQL & Database Fundamentals

  • Advanced SQL: Joins, CTEs, Window Functions, Query Optimization
  • Relational Databases: PostgreSQL, MySQL, SQL Server
  • Data Warehousing: Snowflake, Redshift, BigQuery, Star Schema

You Should Know:

-- Example: Optimized Query with Window Functions 
SELECT 
employee_id, 
department, 
salary, 
AVG(salary) OVER (PARTITION BY department) AS avg_dept_salary 
FROM employees 
WHERE salary > 100000;

2. Big Data & Distributed Computing

  • Apache Spark & PySpark: Large-scale data processing
  • Hadoop Ecosystem: HDFS, Hive, MapReduce
  • Kafka & Streaming Data: Real-time data pipelines

You Should Know:

 PySpark DataFrame Operations 
from pyspark.sql import SparkSession 
spark = SparkSession.builder.appName("example").getOrCreate() 
df = spark.read.csv("data.csv", header=True) 
df_filtered = df.filter(df["salary"] > 50000) 
df_filtered.show()

3. Cloud Technologies & Infrastructure

  • AWS: S3, Redshift, Lambda, Glue
  • GCP: BigQuery, Dataflow, Pub/Sub
  • Azure: Synapse, Data Factory, Databricks

You Should Know:

 AWS CLI to list S3 buckets 
aws s3 ls

GCP gcloud command to check BigQuery datasets 
gcloud bigquery datasets list

4. ETL & Workflow Orchestration

  • Apache Airflow: Pipeline automation
  • DBT: Data transformation
  • CI/CD Pipelines: Automated deployments

You Should Know:

 Airflow DAG Example 
from airflow import DAG 
from airflow.operators.python_operator import PythonOperator 
from datetime import datetime

def etl_process(): 
print("Running ETL Job")

dag = DAG('etl_pipeline', schedule_interval='@daily', start_date=datetime(2023, 1, 1)) 
task = PythonOperator(task_id='run_etl', python_callable=etl_process, dag=dag)

5. Programming (Python, Scala, Java)

  • Python: ETL scripting
  • Scala/Java: High-performance Spark jobs

You Should Know:

// Scala Spark WordCount 
val textFile = sc.textFile("hdfs://...") 
val counts = textFile.flatMap(line => line.split(" ")).map(word => (word, 1)).reduceByKey(_ + _) 
counts.saveAsTextFile("hdfs://...")

6. Data Modeling & Schema Design

  • OLTP vs OLAP
  • Partitioning & Indexing

You Should Know:

-- Creating a Partitioned Table in BigQuery 
CREATE TABLE sales ( 
date DATE, 
product_id STRING, 
revenue FLOAT 
) 
PARTITION BY date;

7. System Design for Data Engineering

  • Batch vs Streaming Architectures
  • RDBMS, NoSQL, or Data Lakes Selection

Check here for structured learning: Bosscoder Academy

What Undercode Say

Mastering these skills requires hands-on practice. Here are additional Linux/Windows commands for data engineers:

 Monitor Hadoop cluster 
hdfs dfsadmin -report

Check Kafka topics 
kafka-topics.sh --list --zookeeper localhost:2181

Azure Blob Storage access 
az storage blob list --account-name <storage_account> --container-name <container>

Windows: Check running services 
Get-Service | Where-Object { $_.Status -eq "Running" }

Expected Output:

A well-prepared Data Engineer with expertise in SQL, Big Data, Cloud, and ETL can secure top-tier roles at companies like Meesho or Flipkart. Continuous learning and practical implementation are key.

References:

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