We are looking for a highly skilled, hands-on Senior Data Engineer with deep expertise in SQL Server, T-SQL development, data modelling, and database performance optimization. This role is primarily focused on designing, developing, and optimizing scalable data solutions that support operational systems, analytics, and application workloads. The ideal candidate is an expert-level SQL developer who understands how to structure, tune, and manage large-scale relational data platforms efficiently.
What you'll do:- Data Engineering Leadership, Standards & Quality
- Act as a senior technical authority for data engineering, defining and enforcing best practices
- Contribute to and lead code reviews for SQL, ETL pipelines, and data transformations to ensure maintainability, performance, and consistency.
- Establish standards for naming conventions, data structures, documentation, and version control across the data platform.
- Mentor and guide team members on data engineering techniques, performance tuning, and design patterns.
- Promote a culture of high-quality, testable, and observable data solutions.
- Data Pipeline Development & Integration
- Design, develop, and maintain scalable data pipelines using Azure Data Factory (ADF), including pipelines, data flows, triggers, and parameterization.
Integrate data from APIs, flat files, databases, and cloud/on-prem systems.
- Implement robust ingestion patterns for structured and semi-structured data (JSON, XML, CSV).
- Ensure reliable, efficient, and secure movement of data across systems.
- Data Modelling & Transformation
- Design and maintain both normalized (OLTP-aligned) and denormalized (analytical / reporting) data models.
- Apply best practices in dimensional modelling (fact/dimension tables) as well as normalized relational design.
- Implement transformations using SQL (T-SQL), stored procedures, and data flows to prepare analytics-ready datasets.
- Ensure data models are scalable, reusable, and aligned with business requirements.
- Manage historical data tracking, including slowly changing dimensions and auditability.
- Performance, Reliability & Scalability
- Optimize SQL queries, ETL pipelines, and data storage for large datasets (millions+ rows).
- Implement indexing strategies, partitioning, and efficient data access patterns.
- Ensure pipelines are resilient with proper error handling, retry logic, and monitoring.
- Design solutions that minimize impact on transactional systems (clear separation of OLTP and reporting workloads).
- Proactively identify and resolve performance bottlenecks.
- Application & API Integration
- Collaborate closely with backend (.NET) teams to support data access patterns and integration with application services.
- Design and deliver aggregated datasets and data structures optimized for API consumption.
- Support frontend (e.g., Vue.js) data requirements by enabling efficient querying, filtering, and pagination.
- Contribute to embedded analytics and application-driven reporting use cases.
- Collaboration & Continuous Improvement
- Work closely with BI developers, analysts, and stakeholders to translate data requirements into scalable solutions.
- Continuously improve data platform architecture, tooling, and processes.
- Support CI/CD practices for data pipelines and deployments.
- Stay current with evolving data engineering tools, patterns, and best practices.
Education & Qualifications:- Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related field, or equivalent practical experience.
- Demonstrated experience delivering end-to-end data engineering solutions in production environments.
- Proven experience contributing to code reviews, enforcing standards, and improving engineering practices.
Nice to Have:
- Experience in financial services, asset management, or data-intensive domains.
- Familiarity with event-driven architectures and real-time data processing.
- Understanding of downstream analytics and reporting requirements.