Senior Data Engineer

About Aiviq

Aiviq is a cutting-edge fintech company revolutionising financial services and asset management. We empower the world's leading asset managers with data-driven insights and innovative technology solutions. Our cloud-based platform transforms complex financial data into actionable intelligence, addressing critical challenges in client data quality and insights. Serving global asset managers overseeing trillions in Assets under Management, we're at the forefront of financial technology innovation.

Reporting Structure

Reports to: Data Centre of Excellence Team Lead Dotted line to: Head of Engineering Location: UK (Hybrid)

Job Purpose

We're seeking an accomplished Data Engineer to join our Data Centre of Excellence while working closely with our Engineering team on our sophisticated financial data management platform. This role combines the technical depth of enterprise data engineering with the fast-paced delivery demands of product development, requiring someone who is adept at translating business logic into code, can think architecturally while attending to implementation details. You'll be the bridge between our data architecture standards and practical product delivery, ensuring our financial data pipelines are robust, performant, and built on solid engineering principles.

Key Responsibilities

Data Engineering & Development

Design, build, and optimize data pipelines across Microsoft SQL Server and Azure Synapse Analytics environments

Develop and maintain Spark SQL notebooks for complex data transformations and analysis

Translate business logic and financial calculation requirements into clear, maintainable code

Create data integrity checking scripts and validation frameworks in collaboration with QA teams

Implement automated data quality checks and reconciliation processes

Assist with the maintenance of a curated, anonymized dataset for system testing that covers all known scenarios and edge cases

Analyse production datasets to identify anomalies, debug stored procedures and notebooks, and resolve data quality issues

Demonstrate tenacity in investigating root causes, diving deep into complex problems until resolution is achieved

Architecture & Performance

Consult on database architecture decisions, balancing performance, scalability, and maintainability

Optimize query performance and data processing workflows for large-scale financial datasets

Design and implement solutions using Azure Data Factory, Delta Lake, and related technologies

Think end-to-end about data flows while ensuring rigorous attention to implementation details

Documentation & Process

Create and maintain comprehensive documentation of database schemas, processes, and data flows

Develop visual process models using tools such as Lucidchart, Visio, dbt, Azure Purview, or similar platforms

Document data transformation logic and calculation methodologies for audit and compliance purposes

Contribute to data governance standards and best practices across the organization

Production Support & Collaboration

Act as first point of escalation for high-priority data issues in production environments

Partner with test automation engineers to develop data-driven testing strategies and create data integrity checking scripts

Collaborate across engineering teams using Azure DevOps for CI/CD pipeline development

Support both Data CoE initiatives and product engineering priorities through effective stakeholder management

Required Skills & Experience

Technical Expertise

Database Technologies: Strong proficiency in MS SQL Server and Azure Synapse Analytics

Big Data Processing: Hands-on experience with PySpark, Spark SQL, and notebook-based development

Cloud Platforms: Demonstrable experience with Azure ecosystem (Synapse, Data Factory, Delta Lake)

Programming: Solid coding skills in SQL, Python, and/or C#

Version Control: Experience with Git and Azure DevOps or similar CI/CD platforms

Testing: Experience building out unit and integration test frameworks and processes to ensure pipelines and notebooks and other code artefacts are fully automation-tested

Domain Knowledge

Ideally a proven track record working with complex financial data and calculations

Understanding of financial data structures, reconciliation processes, and audit requirements

Experience handling temporal data, slowly changing dimensions, and historical data management

Knowledge of data quality frameworks and validation methodologies

Professional Capabilities

Strong analytical and debugging skills for complex data scenarios across stored procedures and notebooks

Relentless problem-solving approach - comfortable digging deep into technical issues and pursuing answers until problems are fully understood and resolved

Experience with testing principles and data integrity validation

Ability to consult on technical architecture while maintaining pragmatic focus

Excellent documentation and process modelling capabilities

Experience Level

5+ years in data engineering roles with increasing responsibility

Track record of delivering production data systems at scale

Experience working in matrix or cross-functional team structures

Desirable Skills

Knowledge of Azure Purview or data cataloguing solutions

Familiarity with Great Expectations or similar data quality frameworks

Understanding of behaviour-driven development for data testing

Familiarity with data anonymization, masking, and synthetic data generation techniques

Experience with GraphQL APIs and modern application data layers

Exposure to modern data visualization tools (Power BI, Tableau)

Experience with data build tool (dbt) or similar transformation frameworks

Personal Attributes

Essential

Collaborative mindset: Comfortable working across teams with different priorities and technical backgrounds

Detail-oriented: Meticulous about data accuracy while maintaining delivery momentum

Investigative nature: Thrives on troubleshooting complex issues and pursuing problems through multiple layers until fully resolved

Intensely curious: Demonstrates deep curiosity about existing processes and systems, with a natural drive to investigate how things work and independently acquire new knowledge

Pragmatic problem-solver: Balances architectural thinking with practical implementation

Clear communicator: Articulates technical concepts to both specialist and generalist audiences

Ownership mentality: Takes responsibility for production systems and follows through on commitments

Cultural Fit

Thrives in a matrix organization with multiple stakeholders

Comfortable with ambiguity and competing priorities

Passionate about engineering excellence and continuous improvement

Values documentation and knowledge sharing

Responds well under pressure during production incidents

What We Offer

Professional Development

Exposure to enterprise-scale financial data systems handling complex calculations

Opportunity to shape data engineering standards across the organization

Work with modern Azure cloud infrastructure and emerging technologies

Collaborate with skilled engineers across test automation, software development, and data teams

Matrix structure providing diverse learning opportunities from both CoE and product perspectives

Work Environment

Hybrid working arrangement with flexibility

Collaborative engineering culture valuing quality and craftsmanship

Investment in tools, training, and professional growth

Meaningful work on systems that impact financial data integrity and business decisions