Mid-level Data Engineer – Optimisation

Candidate Description

Marked by a strong record of large-scale data transformation, this Senior Data Engineer brings over seven years of hands-on experience architecting high-performing pipelines across banking, healthcare and enterprise collaboration platforms. Leveraging deep expertise in Databricks, Apache Spark, advanced SQL and multi-cloud ecosystems (Azure and AWS), they consistently deliver resilient, analytics-ready data foundations for organisations such as a Major Bank, a Technology Company and a Healthcare Company.

Their technical acumen spans end-to-end pipeline engineering, a Data Storage Solution optimisation, SQL performance tuning, and implementation of data-quality frameworks. Notable achievements include reducing query execution times by up to 40 %, lowering compute costs by 20–30 % through partitioning strategies, and enabling Power BI datasets that refresh 30 % faster. Adept at cloud modernisation, they have led migrations from legacy ETL to scalable architectures using Azure Data Services, AWS S3 and Databricks, ensuring seamless ingestion, transformation and reporting layers.

Beyond technical prowess, this professional demonstrates leadership by mentoring junior engineers, collaborating with cross-functional teams and consistently earning recognition such as an Award for Leadership & Quality Delivery. Their ability to adapt across consulting, financial-services, automotive and healthcare environments positions them as a versatile asset capable of driving data-centric transformation and delivering tangible business value.