Senior Data Engineer

Responsibilities

. Integrate data from multiple sources, such as databases, APIs, or streaming platforms, to provide a unified view of the data

. Implement data quality checks and validation processes to ensure the accuracy, completeness, and consistency of data

. Identify and resolve data quality issues, monitor data pipelines for errors, and implement data governance and data quality frameworks

. Enforce data security and compliance with relevant regulations and industry-specific standards

. Implement data access controls, encryption mechanisms, and monitor data privacy and security risks

. Optimise data processing and query performance by tuning database configurations, implementing indexing strategies, and leveraging distributed computing frameworks

. Optimize data structures for efficient querying and develop data dictionaries and metadata repositories

. Identify and resolve performance bottlenecks in data pipelines and systems

. Collaborate with cross-functional teams, including data scientists, analysts, and business stakeholders

. Document data pipelines, data schemas, and system configurations, making it easier for others to understand and work with the data infrastructure

. Monitor data pipelines, databases, and data infrastructure for errors, performance issues, and system failures

. Set up monitoring tools, alerts, and logging mechanisms to proactively identify and resolve issues to ensure the availability and reliability of data

. It would be a plus if he has software engineering background

Requirements

. Bachelor's or master's degree in computer science, information technology, data engineering, or a related field

. Strong knowledge of databases, data structures, algorithms

. Proficiency in working with data engineering tools and technologies including knowledge of data integration tools (e.g., Apache Kafka, Azure IoTHub, Azure EventHub), ETL/ELT frameworks (e.g., Apache Spark, Azure Synapse), big data platforms (e.g., Apache Hadoop), and cloud platforms (e.g., Amazon Web Services, Google Cloud Platform, Microsoft Azure)

. Expertise in working with relational databases (e.g., MySQL, PostgreSQL, Azure SQL, Azure Data Explorer) and data warehousing concepts.

. Familiarity with data modeling, schema design, indexing, and optimization techniques is valuable for building efficient and scalable data systems

. Proficiency in languages such as Python, SQL, KQL, Java, and Scala

. Experience with scripting languages like Bash or PowerShell for automation and system administration tasks

. Strong knowledge of data processing frameworks like Apache Spark, Apache Flink, or Apache Beam for efficiently handling large-scale data processing and transformation tasks

. Understanding of data serialization formats (e.g., JSON, Avro, Parquet) and data serialization libraries (e.g., Apache Avro, Apache Parquet) is valuable

. Having experience in CI/CD and GitHub that demonstrates ability to work in a collaborative and iterative development environment

. Having experience in visualization tools (e.g. Power BI, Plotly, Grafana, Redash) is beneficial

Preferred Skills & Characteristics

Consistently display dynamic independent work habits, goal oriented, passionate in growth mindsets and self-motivated professional. Self-driven and proactive in keeping up with new technologies and programming