Data Engineer
Our data engineers design, build, and maintain the robust data infrastructure that collects, stores, processes, and analyses your most critical asset information. They enable real time processing, power personalised customer experiences, and provide the foundation for data driven decision making. Their expertise in diverse frameworks and tools is vital for enhancing the efficiency of your entire data ecosystem.
About This Role
Sonaqode's Data Engineers build the robust data infrastructure that fuels business growth, transforming raw data into strategic assets, unlocking new opportunities, and driving measurable revenue. Our experts design and implement sophisticated data pipelines and models that turn complex information into actionable insights for smarter decision-making and optimised operations.
They prioritise data integrity and system performance to ensure reliable accessibility, applying meticulous attention to detail and a commitment to delivering data solutions that directly support your business objectives. Our dedicated engineers bring deep expertise in SQL, Python, Spark, and Hadoop, with proven experience integrating cloud platforms, machine learning, and big data technologies to achieve exceptional data quality and reliability.
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Skill Set
Technical Skills
- Programming: Proficient in Python, Java, and Scala for data processing and manipulation.
- SQL: Strong SQL skills for querying and managing relational databases.
- Big Data Technologies: Experience with Hadoop, Spark, and other big data frameworks.
- Data Pipelines: Ability to build and maintain ETL/ELT pipelines using tools such as Airflow, Luigi, and Kafka.
- Cloud Platforms: Good knowledge of cloud based data services on AWS, Azure, and GCP.
- Data Warehousing and Modeling: Good understanding of data warehousing concepts and building dimensional models.
- Data Quality: Ability to ensure data accuracy, completeness, and consistency.
Experience
- Data engineering projects: Hands-on experience in building and maintaining data pipelines.
- Big data processing: Working with large datasets and distributed systems.
- Data warehousing: Designing and implementing data warehouses.
- Cloud technologies: Skilled at utilising cloud based data services for efficient data management and migrating data to cloud platforms.
- Data governance: Implementing data security measures and compliance standards.
Key Deliverables
Data Infrastructure and Pipelines
- Data Ingestion: Developing pipelines to extract data from various sources databases, APIs, files.
- Data Transformation: Cleaning, transforming, and standardising data for analysis.
- Data Storage: Designing and implementing data storage solutions data warehouses, data lakes.
- Data Pipelines: Building automated data pipelines for efficient data movement and processing.
Data Modeling and Warehousing
- Data Modeling: Creating data models and schemas for effective data organisation.
- Data Warehousing: Designing and implementing data warehouses or data marts.
Data Quality and Governance
- Data Quality Assurance: Implementing data quality checks and validation processes.
- Data Governance: Establishing data governance policies and standards.
Cloud Integration and Optimisation
- Cloud Integration: Integrating data solutions with cloud platforms AWS, GCP, Azure.
- Cost Optimization (Optimisation): Optimising data storage and processing costs.