Data Analyst

SUMMARY:
Our client is seeking an experienced and passionate Data Engineer to design and build robust data infrastructure that facilitates large data volumes. The role requires a blend of database admin skills (work with and manage relational databases) and data engineering (taking data from various datasets and collections to clean, store, transform and analyse). You will work closely with business analysts and software engineers to ensure efficient data pipelines, quality data sources, and scalable solutions that support key business decisions and innovation.

POSITION INFO:
Responsibilities:

• Assembling large, complex sets of data that meet non-functional and functional business requirements
• Design, develop, monitor and maintain scalable data pipelines and ETL processes
• Build required infrastructure for optimal extraction, transformation and loading of data from various data sources using Integration and SQL technologies, often these are Cloud based
• Identifying, designing and implementing internal process improvements including redesigning infrastructure for greater scalability, optimizing data delivery, and automating manual processes
• Building analytical tools to utilise the data pipeline, providing actionable insight into key business performance metrics
• Ensure data quality, consistency, integrity, and security across all systems
• Drive continuous improvement of data engineering best practices and tooling

• Bachelor’s Degree in Computer Science, Engineering, Mathematics, or related field
• 5-7 years of experience in a database management, data engineering or similar role
• Proficiency in programming languages such as Python or Scala
• Strong proficiency in SQL and experience with relational databases (e.g. MSSQL, PostgreSQL, MySQL)
• Hands-on experience with No-SQL database technologies
• Experience in database optimization and performance tuning required
• Good understanding of Data Integration patterns
• Exposure to BI tooling such as Power BI or Yellowfin is advantageous
• Experience setting up MS SQL Replication and Data Archiving strategies will be beneficial
• Experience with cloud platforms (e.g., AWS, GCP, Azure) and services like S3, Lambda, Redshift, BigQuery, or Snowflake
• Familiarity with big data technologies like Apache Spark, Data Bricks, and Hive
• Familiarity with data modelling, warehousing concepts, and data governance practices
• Exposure to Data cleansing and de-duplication techniques will be beneficial

• Experience with stream processing tools (e.g., Kafka, Spark Streaming, Flink)
• Knowledge of containerization (Docker) and orchestration tools (Kubernetes)
• Understanding of CI/CD principles and infrastructure-as-code
• Exposure to machine learning workflows and MLOps