SQL Trainer / SQL Tutor with 8 To 10 years of industry experience

SQL & Database Engineering Tutor / Trainer

Location: Hyderabad, Telangana On-site

Experience: 810 Years

Engagement: Long-term teaching engagement 23 years

Schedule: Monday–Saturday, 9:00 AM–2:00 PM

Batch Size: 80–100 students

Batch Duration: 3 months

About the Program

We are running a practical Data Engineering Skill Development Program designed to provide graduates with industry-relevant

skills in Python, SQL, Data Engineering and Generative AI. Each batch runs for three months and typically consists of

80–100 students. The program combines classroom learning with hands-on practice, assignments, assessments, mentoring

and an integrated capstone project. We are looking for an experienced SQL & Database Engineering Tutor who can take

ownership of the SQL curriculum and build strong SQL and database fundamentals that directly support students’ Data

Engineering journey.

Key Responsibilities

• Conduct classroom sessions Monday–Saturday, 9:00 AM–2:00 PM at our training institute in Hyderabad.
• Teach SQL from fundamentals through advanced, Data Engineering-oriented SQL.
• Conduct hands-on SQL practice using realistic business datasets.
• Explain database concepts using real-world application and Data Engineering examples.
• Design, assign and evaluate practical exercises and assignments.
• Conduct doubt-clearing and problem-solving sessions.
• Mentor students during SQL projects and the integrated Data Engineering project.
• Evaluate students through assignments, quizzes, practical assessments and projects.
• Coordinate with Python and Data Engineering tutors to ensure smooth curriculum handoffs.
• Help students understand both how to write SQL and why a particular approach is appropriate for a Data Engineering problem.
• Use realistic datasets such as customers, loans, transactions and payments.
• Continuously improve teaching material and practical exercises.

Curriculum

1. Database Fundamentals

• Database, DBMS and RDBMS
• Tables, rows and columns
• Primary/foreign keys and relationships
• NULL and data types
• Constraints
• Application ® Service ® Database architecture
• Operational vs analytical databases
• OLTP vs OLAP

2. SQL Fundamentals

• SELECT, DISTINCT, WHERE, ORDER BY
• Filtering and logical operators
• IN, BETWEEN, LIKE
• NULL handling
• Aliases and expressions

3. DDL, DML, DQL, TCL & DCL

• CREATE, ALTER, DROP, TRUNCATE
• INSERT, UPDATE, DELETE, SELECT
• COMMIT, ROLLBACK, SAVEPOINT
• GRANT and REVOKE

4. Constraints

• Primary Key
• Foreign Key
• UNIQUE
• NOT NULL
• CHECK
• DEFAULT
• Referential integrity

5. SQL Functions

• String, numeric and date functions
• Conversion functions
• CASE
• COALESCE and NULLIF
• Data cleansing with SQL

6. Aggregations

• COUNT, SUM, AVG, MIN, MAX
• GROUP BY and HAVING
• Business-oriented aggregation

7. Joins

• INNER, LEFT, RIGHT and FULL JOIN
• CROSS and SELF JOIN
• Join cardinality
• One-to-many/many-to-many
• Duplicate explosion and NULL behaviour
• Join troubleshooting

8. Advanced SQL

• Scalar, multi-row and correlated subqueries
• EXISTS / NOT EXISTS
• UNION / UNION ALL / INTERSECT / EXCEPT
• CTEs and multiple CTEs
• Recursive CTE introduction

9. Analytical SQL

• OVER, PARTITION BY and ORDER BY
• ROW_NUMBER, RANK, DENSE_RANK, NTILE
• LAG and LEAD
• Running totals and moving averages
• Top-N and month-over-month analysis

10. SQL for Data Engineering

• Raw, staging, transformation and target tables
• Full vs incremental loads
• Upsert / MERGE
• Watermarks / high-water marks
• Idempotency
• Deduplication
• Source-to-target transformations

11. Data Modeling

• ER modeling
• Normalization and denormalization
• Facts and dimensions
• Grain and measures
• Star and snowflake schemas
• Natural vs surrogate keys

12. Slowly Changing Dimensions

• SCD Type 1
• SCD Type 2
• Effective/end dates
• Current flags
• Historical data management
• SQL implementation

13. SQL Performance

• Indexes and composite indexes
• Selectivity
• EXPLAIN / execution plans
• Query, join and filter optimization
• Partitioning concepts

14. Data Quality & Production SQL

• Duplicate and NULL checks
• Referential integrity
• Source-to-target reconciliation
• Views and materialized views
• Transactions and ACID
• Basic security
• Brief exposure to procedures, functions and triggers

Candidate Profile — Must Have

• 8–10 years of professional experience working extensively with SQL and relational databases.
• Strong hands-on experience with complex SQL and analytical SQL.
• Strong understanding of database concepts and data modeling.
• Experience with joins, CTEs, window functions and large datasets.
• Good understanding of ETL/ELT and Data Engineering concepts.
• Experience with data warehousing and dimensional modeling.
• Strong communication and classroom presentation skills.
• Ability to explain complex technical concepts simply.
• Passion for mentoring and developing students.
• Willingness to commit to a 2–3 year teaching engagement.
• Ability to teach Monday–Saturday, 9:00 AM–2:00 PM, on-site in Hyderabad.

Preferred

• Experience as a Data Engineer, Analytics Engineer, Data Analyst, BI Engineer or Database Engineer.
• Experience with MySQL, PostgreSQL, Oracle, SQL Server, Redshift, Snowflake or similar platforms.
• Experience with cloud data platforms and Data Engineering projects.
• Prior corporate training or classroom teaching experience.
• Experience designing SQL assignments and assessments.