ETL Developer

Job Description

Current JD – Data Quality Engineer (Role). Ready to work as DQE. 1. ETL Testing Experience , 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.