Data Engineer Python and SQL ,numPy

Job Overview We are looking for an individual who has at least a basic to intermediate understanding of SQL, Python, Pandas, and NumPy. It would be a plus to have knowledge of advanced-level concepts, but it's not a strict requirement. Key Responsibilities

• Extract, transform, and analyze, manipulate, restructure data using SQL and Python
• Clean, filter, transform, and reshape data using Pandas
• Perform numerical computations and vectorized operations using NumPy
• Create scripts to automate data loading and preprocessing tasks
• Collaborate with team members to understand data needs and deliver insights
• Prepare and present basic analytical reports or summaries

__________________________________________________________________________________ Core Skills – Must Have=>SQL (Basic to Intermediate): SQL Skills

• Core SQL Clauses — Must Know

Proficiency In Using SELECT, WHERE, GROUP BY, ORDER BY, LIMIT/OFFSET, TOP, HAVING

• Different types of Operators.
• Joins — Very Important

Must Have Hands-on Knowledge Of INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL OUTER JOIN, should understand how improper join conditions can lead to duplicate data.

• Functions — Must Know Built-in functions: Aggregate, Analytical, String, Date functions User Defined Functions (UDFs)
• Filtering Techniques — Must Know

Use Of BETWEEN, IN, LIKE, IS NULL

• Subqueries & Common Table Expressions (CTEs) — Must Know
• Window Functions — Preferred (Good to Have), should know how it can be used in analytical and aggregate functions. (Good to have)
• Basic Schema Design — Must Know Understanding of: Tables and relationships, Primary and foreign keys, Data types

=>Python (Basic to Intermediate — Very Important):

• Data types: lists, dictionaries, tuples, sets
• Loops, list comprehensions, and conditional statements
• Functions, lambda expressions, and exception handling
• File I/O operations (working with CSV, Excel, JSON, and text files)
• Basics of modular programming (importing custom Python files/modules)
• Using virtual environments and package management (pip)
• Familiarity with Jupyter notebooks and script-based workflows

=>Python's Pandas (Basic to Mid-Level-— Very Important):

• Reading data from CSV, Excel, JSON, and databases
• Creating, indexing, slicing, and filtering DataFrames
• Applying groupby(), pivot(), pivot_table() for aggregation
• Handling missing data: fillna(), dropna(), isnull()
• Merging, joining, and concatenating DataFrames
• Applying custom functions using apply() and lambda
• Data type conversions and column renaming
• Working with datetime fields and string operations
• Writing data back to file or database

=>Python's NumPy (Basic to Mid-Level— Very Important):

• Creating and reshaping arrays (reshape, flatten, transpose)
• Element-wise operations and broadcasting
• Indexing, slicing, and conditional selection
• Aggregations: mean, sum, max, min, std
• Random number generation (np.random)
• Stack/split arrays (vstack, hstack, split)
• Basic linear algebra: dot product, matrix multiplication (optional)

Last and most important concept- How to write, secure and optimized code, queries using Python and SQL.