Masthanvali Sheik

Data Engineer with 4+ years of experience designing and delivering cloud data platforms across retail, insurance, healthcare, and education. Strong hands-on experience with Azure Databricks, Microsoft Fabric, Azure Data Factory, Synapse Analytics, PySpark, SQL, and Delta Lake. Experienced in building scalable ingestion and transformation pipelines, implementing Bronze, Silver, and Gold architectures, and delivering analytics-ready datasets for Power BI and downstream consumers.

Comfortable working across the full data lifecycle, from business requirements and source analysis through engineering, governance, performance optimisation, and production support.

Data Platform Developer - Innovic Solutions - Sunderland City Council

(2025-06)

• Partnered with business stakeholders to define data requirements, KPIs, and reporting needs, producing source-to-target mappings and metadata documentation for retail datasets.
• Designed and implemented Lakehouse architecture in Microsoft Fabric, leveraging OneLake for unified storage, Lakehouse tables, and Warehouse for scalable ingestion and analytics workloads.
• Designed Gold-layer data models for council operational and service datasets, supporting reporting across areas such as housing, revenues and benefits, environmental services and wider council operations.
• Developed PySpark + SQL notebooks within Fabric, orchestrated through pipelines to support batch processing and incremental data loads across multiple domains.
• Implemented OneLake shortcuts to share trusted datasets between workspaces without duplicating storage, simplifying cross-domain data access for analytics teams.
• Optimised Lakehouse workloads through partitioning, file compaction, and query tuning, reducing processing times for large joins and aggregations by approximately 20–25%.
• Designed optimized Gold-layer datasets with pre-aggregated and denormalized structures, improving query performance for reporting and analytics workloads.
• Managed notebook execution modes by utilizing high-concurrency sessions for shared development workloads and standard sessions for compute-intensive processing.
• Identified and mitigated Fabric capacity and concurrency issues, controlling pipeline parallelism, optimizing workload scheduling, and reducing overlapping executions to improve stability.
• Applied workspace roles, dataset permissions, and secure sharing patterns to ensure governed access to business-sensitive data.
• Developed Power BI semantic models on top of Gold datasets using DirectLake connectivity, enabling low-latency analytics and self-service reporting.
• Used Fabric monitoring, lineage, notebook logs, and capacity metrics to investigate failed runs and performance issues, improving reliability of scheduled pipelines.
• Used Fabric Variable Libraries to manage environment-specific configurations and support parameterised deployments across Dev, Test, and Production, reducing hard-coded values in notebooks and pipelines.

Data Engineer - Web Resource Limited - Allstate

(2022-10 - 2024-09)

Supported an insurance data platform processing policy, claims, customer, and pricing data used by actuarial, fraud, and reporting teams.

• Engineered PySpark-based transformation frameworks for multi-terabyte insurance datasets, enabling modular, reusable, and parallel execution across multiple domains.
• Developed Delta Live Tables pipelines with schema enforcement and automated data quality checks, improving consistency of curated datasets before they reach curated layers.
• Tuned Spark workloads using partitioning, broadcast joins, caching, and shuffle optimisation, reducing execution times for several critical pipelines by around 25–30%.
• Managed Databricks compute by separating development and production workloads and selecting cluster configurations appropriate to transformation complexity, helping control costs.
• Leveraged Databricks SQL endpoints (serverless querying) on Gold Delta tables for ad-hoc analytics and reporting, enabling low-latency insights without spinning up heavy clusters.
• Configured Maven libraries in clusters to support event-driven ingestion via Azure Event Hub, enabling robust batch and near real-time pipelines.
• Integrated dbt with Databricks to develop modular SQL-based transformations and dimensional data models on Delta Lake, using reusable models, tests, and documentation to improve maintainability and data quality.
• Applied Unity Catalog governance, including table-level and workspace-level access control, secure data sharing, and lineage tracking for compliance-sensitive insurance datasets.
• Built feature engineering pipelines for pricing and fraud models, producing curated ML-ready datasets for actuarial teams and data scientists.
• Monitored workloads using job run logs, cluster metrics, and Delta Lake statistics, identifying bottlenecks, optimizing compute usage, and controlling costs across multiple workloads.

BI Engineer - Web Resource Limited - Optum (UnitedHealth Group)

(2021-04 - 2022-09)

Worked on the migration of healthcare reporting data from on-premises SQL Server, Oracle, and flat-file sources into Azure-based analytics platforms.

• Built parameterised Azure Data Factory pipelines to ingest patient, claims, provider, and operational datasets into ADLS Gen2 and Synapse Analytics.
• Developed metadata-driven ingestion patterns using control tables and dynamic datasets, reducing duplication across pipelines and making new source onboarding more straightforward.
• Created Synapse notebooks to cleanse and transform healthcare datasets, including patient normalisation, claims enrichment, and provider-to-patient relationships.
• Designed analytics-ready tables in Synapse to support operational reporting and Power BI dashboards used by healthcare teams.
• Implemented Managed Identities, Azure Key Vault, and private networking patterns to secure sensitive datasets and align with organisational data protection requirements.
• Designed Power BI semantic models using star-schema principles, defining relationships, hierarchies, calculated columns, and reusable DAX measures to support operational and management reporting.
• Developed interactive Power BI dashboards with drill-through, bookmarks, tooltips, and KPI’s, enabling users to analyse performance from high-level trends through to detailed records.
• Implemented Row-Level Security (RLS) and optimised Power BI models by reducing unnecessary columns, improving DAX calculations, and managing model relationships to improve report performance and secure access to sensitive data.
• Published and maintained Power BI reports and semantic models in Power BI Service, configured scheduled refreshes and gateways, and monitored refresh failures and alerts to troubleshoot data source, credential, and refresh issues.

Master’s degree (MSc) - Engineering Management (2026)