Data Engineer / Developer

About the jobThe purpose of this role is to develop required software features, achieving timely delivery in compliance with the performance and quality standards of the company.Technical SkillsData Cleaning Tools & Libraries: Proficiency with tools and libraries to clean and preprocess data for example:PythonRSQLExcel- emphasis on Familiarity with data cleaning functions, filters, and pivot tables.2. Data Management & Analysis SkillsData Validation & Consistency: Ability to identify data quality issues such as duplicates, missing values, outliers, and inconsistencies.Data Transformation: Experience in transforming raw data into usable formats, including reshaping, aggregating, or normalizing data.Handling Missing Data: Familiarity with imputation techniques or ways to deal with incomplete datasets.Data Normalization & Standardization: Ensuring uniformity in data formats, units of measurement, and naming conventions.Data Aggregation: Summarizing or grouping data for analysis and ensuring that it is consistent across all sources.3. Knowledge of Data QualityData Integrity: Understanding the importance of maintaining accurate and consistent data over time.Data Profiling: Identifying patterns, anomalies, and key characteristics of the dataset.Error Detection: Ability to find and correct errors within datasets by checking for outliers, misclassifications, or missing values.4. Soft SkillsAttention to Detail: The ability to identify small inconsistencies and issues within large datasets.Problem-Solving: Being resourceful in resolving data issues and proposing solutions.Critical Thinking: Analyzing data in-depth and understanding its implications.Communication: Ability to explain data issues and cleaning steps to non-technical stakeholders.5. Experience with Data FormatsStructured Data: Familiarity with both structured (tables, databases)Data Sources: Ability to clean data from various sources such as spreadsheets, databases, APIs, logs, etc.File Formats: Proficiency in working with common data file formats like CSV, XML, and Excel.6. Statistical and Analytical SkillsBasic Statistics: Understanding of basic statistical concepts to spot anomalies, outliers, or trends in data.Data Visualization: Ability to visualize the cleaned data to identify trends and patterns (e.g., with Power BI .7. Automation and ScriptingAutomating Repetitive Tasks: Experience in automating data cleaning processes with scripts or workflow automation tools.Batch Processing: Capability to clean data in bulk, particularly when dealing with large datasets.