Data Engineer – ETL, SQL & BI

Job Purpose The Data Engineer will support the development, integration, transformation, and reliability of data across multiple business systems and sources. The role will contribute to building and maintaining data pipelines, improving data quality, developing reusable data models, and enabling business intelligence and reporting solutions.Working closely with Data Scientists, Software Engineers, and business stakeholders, the Data Engineer will help transform business requirements into structured, reliable, and scalable data solutions while leveraging modern automation and AI-assisted development tools.Key ResponsibilitiesData Pipelines & IntegrationAssist in designing, developing, maintaining, and monitoring ETL/ELT pipelines for structured and unstructured data.Extract, integrate, and transform data from multiple internal and external sources.Support troubleshooting and optimization of data pipelines to ensure reliable and timely data availability.Data Quality & ReliabilityPerform data cleaning, validation, reconciliation, and quality checks to ensure data accuracy, completeness, and consistency.Identify data anomalies and support root-cause analysis and resolution.Contribute to maintaining reliable and trusted datasets for analytics and reporting.Data Modeling & TransformationSupport the design and development of clean, reusable, and scalable datasets and semantic layers.Apply fundamental data modeling concepts, including dimensional modeling, star schemas, and data warehouse principles.Help ensure consistent business definitions and data structures across reporting and analytical solutions.Apply appropriate software design patterns and SOLID principles when developing reusable data components.Business Intelligence & VisualizationCollaborate with business teams to develop interactive dashboards, automated reports, and KPI tracking solutions.Support dashboard development and visualization using Tableau or similar BI tools.Translate business requirements into meaningful data models, metrics, and reporting outputs.Cross-Functional CollaborationWork closely with Data Scientists, Software Engineers, Analysts, and business stakeholders to understand data requirements.Translate business questions and operational requirements into clear and structured data solutions.Support data-driven initiatives across different business functions.AI & AutomationUtilize modern AI-assisted development tools to accelerate data querying, analysis, scripting, and code scaffolding.Validate AI-generated outputs to ensure accuracy, security, reliability, and compliance with organizational data governance policies.Identify opportunities to automate repetitive data and reporting activities.Documentation & GovernanceMaintain clear and accurate technical documentation for data pipelines, transformations, models, and workflows.Develop and maintain data dictionaries, process documentation, and operational procedures.Follow established data governance, security, and quality standards.Required QualificationsEducationBachelor's degree in Computer Science, Data Science, Information Technology, Software Engineering, Computer Engineering, or a related discipline.Technical SkillsSolid foundational knowledge of SQL, including joins, aggregations, subqueries, and data manipulation.Proficiency in at least one programming or scripting language, preferably Python.Understanding of ETL/ELT concepts and data integration processes.Basic understanding of data warehouses, dimensional modeling, and star schemas.Exposure to Tableau or another business intelligence/data visualization platform.Understanding of software development fundamentals, including design patterns and SOLID principles.Familiarity with AI-assisted development or data analysis tools is an advantage.Core CompetenciesStrong analytical and problem-solving abilities.High attention to data accuracy and detail.Ability to translate business requirements into structured technical solutions.Good communication skills with the ability to explain technical concepts to non-technical stakeholders.Strong willingness to learn and develop within data engineering and data architecture.Adaptable and comfortable working with emerging technologies and evolving data environments.