Role Description A Data Engineer / Data Assistant is responsible for supporting the collection, organization, processing, integration, transformation, quality, and availability of business data. The role combines technical data-engineering activities with data-management and administrative support to ensure information is accurate, accessible, secure, and ready for analysis, reporting, and operational use.

Key responsibilities include collecting, organizing, validating, processing, and maintaining structured and unstructured data from databases, applications, APIs, spreadsheets, files, cloud platforms, and other approved sources; developing, maintaining, and optimizing data pipelines and ETL/ELT workflows; extracting data from multiple systems and transforming it into consistent and usable formats; supporting the design and maintenance of data warehouses, data lakes, databases, and data-processing environments; writing and optimizing SQL queries for data extraction, transformation, validation, and reporting; assisting with database administration, data migration, data integration, data synchronization, and system-to-system interfaces; monitoring data pipelines, scheduled jobs, integrations, and automated workflows and investigating failures or inconsistencies; performing data cleansing, deduplication, validation, normalization, reconciliation, and quality-control activities; identifying missing, inaccurate, duplicated, inconsistent, or anomalous data and coordinating corrective actions; maintaining accurate datasets, databases, spreadsheets, data dictionaries, metadata, documentation, and data records; supporting data analysts, business teams, finance, marketing, operations, HR, and management with data requests and reporting requirements; preparing datasets, spreadsheets, summaries, reports, dashboards, and basic visualizations; supporting business-intelligence and analytics activities by preparing clean and reliable datasets; assisting with data governance, data-quality standards, access controls, data privacy, security, and retention requirements; documenting data sources, transformations, workflows, business rules, dependencies, and technical procedures; supporting API integrations,



file transfers, cloud data services, and automated data workflows; using Python, SQL, PowerShell, or other scripting technologies to automate repetitive data-management tasks where appropriate; supporting cloud-based data platforms such as AWS, Microsoft Azure, or Google Cloud; utilizing data orchestration, workflow automation, monitoring, AI-assisted data processing, anomaly detection, and intelligent data-quality tools where appropriate; assisting with data migration, database upgrades, platform implementations, and system-integration projects; monitoring data availability, pipeline performance, processing times, storage capacity, and data-quality indicators; preparing operational and technical reports related to data pipelines, data quality, system performance, and data-management activities; troubleshooting data-processing errors and coordinating with software engineers, database administrators, infrastructure teams, analysts, and business stakeholders; supporting continuous-improvement initiatives to improve data accuracy, reliability, accessibility, automation, and processing efficiency; and maintaining confidentiality and appropriate security controls when handling business, customer, financial, employee, or other sensitive information.

Qualifications

- Diploma or Bachelor's degree in Computer Science, Information Technology, Data Engineering, Data Science, Information Systems, Statistics, Mathematics, or a related discipline.
- Strong understanding of data structures, databases, data processing, data quality, data integration, and data-management principles.
- Strong SQL skills with familiarity with relational databases such as MySQL, PostgreSQL, SQL Server, Oracle, or equivalent platforms.
- Familiarity with Python, Java, Scala, Bash, PowerShell,



or other programming and scripting languages is advantageous.
- Understanding of ETL/ELT processes, data pipelines, data transformation, data validation, and workflow orchestration.
- Familiarity with data warehouses, data lakes, databases, cloud storage, and modern data platforms.
- Knowledge of AWS, Microsoft Azure, Google Cloud, Snowflake, Databricks, BigQuery, or equivalent technologies is beneficial.
- Familiarity with Apache Spark, Airflow, Kafka, dbt, or other data-engineering technologies is advantageous.
- Robust understanding of data cleansing, deduplication, normalization, reconciliation, validation, and data-quality processes.
- Familiarity with Power BI, Tableau, Looker, Excel, or other business-intelligence and reporting tools.
- Strong analytical, numerical, troubleshooting, and problem-solving skills.
- Strong attention to detail when handling large datasets and technical documentation.
- Understanding of APIs, JSON, XML, REST services, file transfers, and system integrations is beneficial.
- Familiarity with data governance, metadata management, data privacy, information security, access control, and data-retention principles.
- Knowledge of automation, AI-assisted data processing, anomaly detection, intelligent data-quality monitoring, and machine-learning data pipelines is advantageous.
- Strong organizational and communication skills with the ability to work with technical and non-technical stakeholders.
- Ability to manage multiple datasets, data requests, pipeline tasks, reports, and deadlines.
- Strong documentation and record-management skills.
- Familiarity with Git, version control, CI/CD, or DevOps practices is beneficial for Data Engineer responsibilities.
- Relevant certifications in cloud computing, databases, data engineering, analytics, or business intelligence are advantageous.
- High level of accuracy, reliability, confidentiality, accountability, and technical discipline.
- Strong commitment to continuous learning and staying informed about cloud data platforms, data engineering, automation, AI, analytics, data governance, and emerging data technologies.

📌 Data Engineer / Data Assistant (Australia)
🏢 Muhammad Luqman
📍 Australia