Senior Engineer, Data Engineering

Job Title -
Data Engineer
Entity -
Aventum
Reports to -
Head of Data
Location -
London/Hybrid
A family that dreams big and always works together to make insurance better. We offer our employees a unique benefits package that includes personal training sessions, well-being provisions, and an additional day off for your birthday! You will work in various settings to build systems that collect, manage, and convert raw data into usable information for data scientists and business analysts to interpret. Your ultimate goal is to make data accessible so that Aventum stakeholders can use it to evaluate and optimise their performance.
Technical and business processes are used to combine data from multiple sources to provide a unified, single view of the data. Partly responsible for accessing, validating, and querying data from various repositories using available tools. Build and maintain data integration processes using SQL Services and other ETL / ELT processes and scripting tools as well as ongoing requests and projects related to the data warehouse, MI, or fast-moving financial data.
Designing the infrastructure/architecture of the big data platform.
Evaluating, comparing and improving the different approaches including design patterns innovation, data lifecycle design, data ontology alignment, annotated datasets, and elastic search approaches
Developing, creating and maintaining a reliable data pipeline and schemas that feed other data processes; includes both technical processes and business logic to transform data from disparate sources into cohesive meaningful and valuable data with quality, governance and compliance considerations.
Customising and managing integration tools, databases, warehouses, and analytical systems.
Design and implement the management, monitoring, security, and privacy of data using the full stack of Azure data services to satisfy business needs.
Ensuring non-functional system characteristics such as such as security, maintainability, quality, performance, and reliability are captured, prioritized, and incorporated into products.
Leverage Agile, CI / CD and DevOps methodologies to deliver high-quality products on time.
Architecting, building, testing, and maintaining data platform. Develop and support a wide range of data transformations and migrations for the whole business.
Design and implement data pipelines, data marts and schemas, access versatile data sources and apply data quality measures.
Monitoring the complete process and applying necessary infrastructure changes to speed up the query execution and analyse data more efficiently; this includes Database optimisation techniques (data partitioning, database indexing and denormalisation) & efficient data ingestion (data mining techniques and different data ingestion APIs).
Responding to errors and alerts to correct and re-process data. Investigate data mismatches and apply solutions. Data scrubbing and analysis to troubleshoot data integration issues and determine root cause.
Bachelor’s degree or equivalent in an engineering/numerate subject (e.g. Engineering, Stats, Maths, Computer Sciences)
Experience in full-stack development and applying it to build science products (E.g. could include some or all Python/R, Linux scripting, SQL, Docker coupled with front ends such as Javascript)
Some experience as a developer building data pipelines and schemas, with data WH implementation, with SQL database development
Stored Procedures, ADF, NoSQL Databases, JSON/XML data formats)
Hands-on experience with Azure Functions, Azure service bus, Azure Data Factory data integration techniques
Knowledge of Data Modelling concepts, monitoring, designs and techniques
Knowledge of Data Warehouse project lifecycle, tools, technologies, and best practices
Experience using Cloud Computing platforms (ADLS Gen2), Microsoft Stack (Synapse, DataBricks, Fabric, Profisee), Snowflake Data Integration, Azure Service Bus, Deltalake, BigQuery, Azure DevOps, Azure Monitor, Azure Data Factory, SQL Server, Azure DataLake Storage, Azure App Service, Apache Airflow, Apache Iceberg, Apache Spark, Apache Hudi, Apache Kafka, Power BI, BigQuery, Azure ML is a plus
Experience with Azure SQL Database, Cosmos DB, NoSQL, MongoDB
Experience with Agile, DevOps methodologies
Awareness and knowledge of ELT/ETL, DWH, APIs (RESTful), Spark APIs, FTP protocols, SSL, SFTP, PKI (Public Key Infrastructure) and Integration testing
Knowledge of Python, SQL, SSIS, and Spark languages.
Demonstrative ability to develop complex SQL queries and Stored Procedures
Relationship-building and stakeholder management
We value applicants from all backgrounds and foster a culture of inclusivity. We understand the need for flexibility, so work in a hybrid model.