DQE With ETL, Python & Advance SQL: Chennai/Pune

Data Quality Engineer with ETL

Pune/Chennai

1. Python /Java Coding (any programming language) - Must have

2. Advance SQL (Mandatory) - Must have

3. Knowledge of BDD (Selenium, Cucumber) - Good to have (Training can be provided)

4. Working knowledge of CI/CD - Good to have (Training can be provided)

5. Hands on Functional Testing Experience - Good to have (Training can be provided)

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.