Current JD – Data Quality Engineer (Role). Ready to work as DQE.

Location – Pune/Chennai

1.                  Experience in Python /Java programming required to automate data analysis tasks - Must have

2.                  Experience in writing Advance SQL queries (Topics listed below) - Must have

3.                  Knowledge of BDD and Cucumber - Good to have

4.                  Knowledge of Agile methodologies, CI/CD etc. - Good to have

5.                  Hands on Functional Data / ETL Testing Experience - Good to have

Advanced SQL topics delve into more complex database operations and query optimization techniques. Here are some key areas to explore:

Window Functions

 RANK, DENSE_RANK, ROW_NUMBER: Assign a rank to each row within a partition.

 LEAD, LAG: Access rows preceding or following the current row within a partition.

 PARTITION BY, ORDER BY: Define partitions and the order within each partition.

 Window frames: Specify the range of rows included in a window function calculation.

Common Table Expressions (CTEs)

 Recursive CTEs: Create recursive queries to process hierarchical data.

 Multiple CTEs: Use multiple CTEs to break down complex queries into smaller, more manageable parts.

Subqueries

 Correlated subqueries: Subqueries that reference columns from the outer query.

 Scalar subqueries: Subqueries that return a single value.

 EXISTS and NOT EXISTS: Test for the existence or non-existence of rows in a subquery.

Analytical Functions

 CUME_DIST: Calculate the cumulative distribution of a value within a group.

 PERCENT_RANK: Calculate the percentile rank of a value within a group.

 NTILE: Divide a result set into a specified number of groups.

Full-Text Search

 Full-text indexes: Create indexes that support full-text search capabilities.

 Ranking functions: Rank search results based on relevance.

 Stop words: Exclude common words from search queries.

Hierarchical Queries

 CONNECT BY PRIOR: Traverse hierarchical relationships in a table.

 START WITH: Specify the starting point for a hierarchical query.

Optimization Techniques

 Indexes: Create appropriate indexes to improve query performance.

 Query tuning: Analyze query execution plans and optimize queries.

 Materialized views: Pre-calculate and store query results for faster access.