Data Engineering Course with Placement Pay after you're hired.

Syllabus

9 modules. 6 months.

1. 01Python for Data EngineeringCore Python through to the depth interviewers actually probe.

   • Intro, variables, data types, casting, strings, booleans
   • Operators, lists, tuples, sets, dictionaries
   • If-else, loops, functions, lambda functions
   • Classes, objects, inheritance, iterators, polymorphism
   • Scope, modules, dates, math, JSON, RegEx, PIP
   • Try-except, user input, file handling
2. 02Pandas & Data WranglingThe library every data interview assumes you know cold.

   • Pandas intro, Series, DataFrames
   • Read CSV, read JSON, analyze data
   • Cleaning empty cells, wrong formats, duplicates
   • Correlations and Pandas plotting
3. 03SQL & Data ModelingThe round that decides most Data Engineering interviews.

   • Select, distinct, where, order by, and/or/not
   • Insert, update, delete, null handling
   • Min/max, count, sum, avg, like, wildcards, in, between, aliases
   • Joins: inner, left, right, self · group by, having, exists, case
   • Stored procedures, operators
   • Create/drop/alter DB & tables, primary & foreign keys, views
   • Window functions, star & snowflake schemas, query optimization
4. 04Apache Spark & PySparkDistributed processing — the single biggest differentiator on your CV.

   • Architecture, SparkSession, SparkContext, cluster managers
   • RDDs, parallelize, repartition/coalesce, broadcast variables, accumulators
   • DataFrames: StructType/StructField, select, collect, withColumn, withColumnRenamed
   • where/filter, drop/dropDuplicates, orderBy, groupBy, joins, union, unionByName
   • UDFs, transform/apply, map/flatMap, foreach
   • SQL functions: aggregate, window, date/timestamp, JSON, pivot, partitionBy, fillna
5. 05AWS for Data EngineeringThe exact services named in current job descriptions.

   • Cloud computing benefits, EC2 (types, pricing, scaling, auto-scaling)
   • S3, RDS, Redshift, Lambda, containers, load balancing, availability zones
   • IAM, CloudWatch, CloudFormation, Elastic Beanstalk, cloud compliance
   • Glue transformations and Redshift warehousing
6. 06Databricks & Delta LakeThe modern lakehouse stack interviews now assume.

   • Workspace, clusters, notebooks, DBFS · interactive vs job clusters
   • Connecting to external sources: S3, ADLS, GCS
   • RDD vs DataFrame vs Dataset · transformations vs actions
   • Delta tables, ACID transactions, schema enforcement & evolution, time travel
   • Medallion architecture (Bronze → Silver → Gold), SCDs, error handling & retries
7. 07Advanced Databricks & OrchestrationProduction-grade pipelines, not notebooks.

   • Autoloader (cloudFiles), CSV/JSON/Parquet ingestion, managed vs external tables
   • Job scheduling, alerts & retries, run history
   • MERGE/UPSERTs, OPTIMIZE & Z-ORDER, VACUUM & data retention
   • Partitioning, bucketing, caching & persisting
   • Multi-task DAGs, Airflow/ADF integration, parameterized pipelines
   • Unity Catalog RBAC, auditing & lineage, Delta Sharing, Delta Live Tables
8. 08Microsoft Azure Data EngineeringThe second cloud that doubles your job-market surface area.

   • IaaS/PaaS/SaaS, regions, availability zones, resource groups, Azure AD & RBAC
   • VMs, App Services, Functions · Blob, ADLS Gen2, File Storage
   • Azure SQL, Cosmos DB, Synapse (dedicated vs serverless SQL pools)
   • Azure Data Factory: Copy Data tool, integration runtime, advanced pipelines
   • Azure Databricks, HDInsight, batch pipeline architectures
   • Purview governance & lineage, App Insights monitoring, cost management
9. 09Interview EngineeringThe module no other institute has — because no one else monitors 50 interviews a day.

   • Live question bank from monitored interviews, refreshed monthly
   • Weekly technical + HR mock interviews, AI-analysed
   • Communication & confidence coaching — scored, not optional
   • Readiness decided on your mock data, not on a sales target

Based on BrowseJobs internal data. Historical figures — not a promise of individual outcome. Hiring depends on the live market and your performance.

3 CV-ready projects

Deployed, not just coded.

Success stories.

How people moved into the role. The path is the same: AI interview, counselling, a course, a retake, then hired.

Real messages from our students.

Words from people who wrote in after they moved.

• Joining third company after course Pranjal
• Support role to Accenture offer
• Received offer letter after doubt Sujit
• Joined Eli Lilly, first salary Ranganayaki
• Joined as Senior Data Engineer gayathrim
• Joining Mphasis Akshay
• Placed, one birthday later Kavi
• Offer received, joining tomorrow Amarnath
• Cracked phData and Alkye Rohith
• Offer letter from ConcertAI Indira
• Offer after clearing all rounds Ajeet
• Landed a job after course Neeraj
• One year since getting placed Harika
• Now lead data engineer on project Gajanan