Solution Architect L3 Full Time

As a Solution Architect (L3), you will be the technical authority bridging business requirements and engineering execution. You will design end-to-end data and application architectures on Microsoft Azure, leveraging Databricks for large-scale data engineering and Power BI for enterprise analytics and reporting. You will lead cross-functional technical discussions, define data platform strategy, and ensure that solutions are scalable, resilient, secure, and aligned with organisational technology goals. This is a high-impact senior individual contributor role requiring deep cloud and data engineering expertise alongside strong stakeholder communication skills.

Key Responsibilities

Solution Architecture & Design

• Define and own end-to-end solution architecture for complex, data-intensive, multi-service systems on Microsoft Azure.
• Create and maintain architecture blueprints, HLDs, LLDs, data flow diagrams, and Architecture Decision Records (ADRs).
• Evaluate build-vs-buy decisions and drive technology selection with clear trade-off analysis across the Azure ecosystem.
• Ensure solutions satisfy non-functional requirements: performance, scalability, reliability, data freshness SLAs, and security.

Data Engineering & Platform Architecture

• Architect end-to-end data pipelines using Azure Databricks spanning ingestion, transformation (Bronze / Silver / Gold layers), and serving.
• Design and govern data lakehouse solutions using Azure Data Lake Storage Gen2 (ADLS Gen2) and Delta Lake.
• Define data modelling standards (star schema, data vault, OBT) aligned to business reporting requirements in Power BI.
• Architect real-time and batch ingestion patterns using Azure Event Hubs, Azure Data Factory (ADF), and Databricks Structured Streaming.
• Drive adoption of Databricks Unity Catalog for data governance, lineage tracking, and fine-grained access control.
• Design cost-efficient Databricks cluster strategies (job clusters, instance pools, spot instances) and monitor DBU consumption.
• Establish data quality frameworks using Great Expectations, Databricks Lakehouse Monitoring, or equivalent tooling.

Analytics & Reporting Architecture

• Design scalable semantic layer and data models powering Power BI enterprise reports and dashboards.
• Define DirectQuery vs Import vs Composite model strategies for optimal Power BI performance and data freshness.
• Architect Power BI Premium / Fabric workspace strategies, including capacity planning and deployment pipelines (Dev / Test / Prod).
• Guide teams on DAX optimisation, row-level security (RLS), and Power BI paginated reports for operational reporting.
• Integrate Power BI with Azure Databricks via Partner Connect or Lakehouse connector for live analytics on Delta tables.

Azure Cloud & Infrastructure

• Architect cloud-native solutions leveraging Azure services: Azure Synapse Analytics, Azure Purview, Azure Key Vault, Azure Monitor, and APIM.
• Define Infrastructure-as-Code (IaC) standards using Terraform or Bicep for repeatable, auditable deployments.
• Design Azure networking topology: VNets, Private Endpoints, NSGs, and hub-spoke patterns to secure data flows.
• Guide teams on containerisation (Docker, Azure Kubernetes Service) and Databricks-native MLflow for ML model management.
• Define disaster recovery, backup, and high-availability strategies across the Azure data platform stack.

Technical Leadership

• Serve as the primary technical point of contact across data engineering, analytics, product, and business teams.
• Provide technical mentorship to senior data engineers, analytics engineers, and tech leads.
• Lead architecture reviews, proof-of-concepts, and data platform migration spikes.
• Drive engineering best practices: modular pipeline design, idempotency, schema evolution, and CI/CD for data workflows.

Stakeholder Engagement & Governance

• Partner with data owners, business analysts, and C-suite stakeholders to translate analytical requirements into robust data products.
• Present platform architecture, roadmaps, and data strategy to executive leadership with clarity and confidence.
• Establish data governance frameworks covering cataloguing (Azure Purview), data classification, and lineage.
• Ensure compliance with data privacy regulations (PDPB, GDPR) and security standards (ISO 27001, SOC 2) across the data platform.

Required Qualifications

Experience

• 8 12 years of overall IT experience, with at least 4 years in a Solution Architect, Principal Data Engineer, or Senior Data Architect role.
• Proven track record of architecting and delivering large-scale data platforms on Microsoft Azure in production.
• Hands-on experience with Azure Databricks including workspace administration, cluster management, Delta Live Tables, and Unity Catalog.
• Strong experience delivering Power BI enterprise analytics solutions semantic modelling, RLS, Premium capacities, and deployment pipelines.
• Experience in Agile / SAFe delivery environments with strong DataOps and CI/CD practices for data pipelines.

Azure Data Platform Proficiency

• Deep expertise in Azure Data Lake Storage Gen2, Delta Lake, and the Medallion (Bronze / Silver / Gold) architecture pattern.
• Proficient in Azure Data Factory for pipeline orchestration, and Azure Event Hubs / Azure Service Bus for streaming ingestion.
• Strong knowledge of Azure Synapse Analytics, Azure SQL Database / Managed Instance, and Cosmos DB.
• Hands-on with Azure Purview (Microsoft Purview) for data cataloguing, classification, and lineage.
• Experience with Azure Key Vault, Azure Active Directory / Entra ID, and Azure Private Link for platform security.
• Familiarity with Azure Monitor, Log Analytics, and Databricks monitoring integrations for observability.

Databricks & Data Engineering

• Expert-level Spark programming in PySpark and/or Scala for large-scale batch and streaming transformations.
• Deep knowledge of Delta Lake features: ACID transactions, time travel, Z-ordering, liquid clustering, and schema enforcement.
• Experience with Databricks Workflows (formerly Jobs), Delta Live Tables (DLT), and Databricks Asset Bundles for CI/CD.
• Familiarity with Databricks MLflow for experiment tracking and model registry (ML workloads are a plus, not mandatory).
• Ability to design cost-optimised cluster policies, autoscaling strategies, and spot-instance configurations.

Power BI & Analytics

• Strong Power BI development skills: data modelling (star schema), DAX measures, calculated columns, and report/dashboard design.
• Experience with Power BI Premium / Fabric capacity management, paginated reports, and dataflows Gen2.
• Proficiency in defining composite model strategies (DirectQuery + Import) and optimising query performance with aggregations.
• Knowledge of Power BI REST APIs, XMLA endpoints, and Tabular Editor for advanced semantic model management.

Soft Skills

• Exceptional written and verbal communication ability to author crisp architecture documents and present confidently to diverse audiences.
• Strong analytical and problem-solving mindset with the ability to navigate ambiguity and competing priorities.
• Collaborative leader who can influence without authority across data engineering, analytics, product, and business stakeholders.

Education & Certifications

• E. / B.Tech / M.Tech in Computer Science, Information Technology, or a related field (or equivalent industry experience).
• Microsoft Certified: Azure Data Engineer Associate (DP-203) required.
• Databricks Certified Associate / Professional Data Engineer strongly preferred.
• Microsoft Certified: Azure Solutions Architect Expert (AZ-305) or Power BI Data Analyst (PL-300) a strong plus.

Preferred Qualifications

• Experience with Microsoft Fabric (OneLake, Fabric Data Engineering, Real-Time Analytics) the strategic evolution of the Azure data platform.
• Exposure to dbt (data build tool) for SQL-based transformation layers and analytics engineering workflows.
• Familiarity with Apache Iceberg or Apache Hudi as alternative open table formats alongside Delta Lake.
• Prior experience with Azure Machine Learning or Databricks Model Serving for operationalising ML models.
• Background in FinTech, HealthTech, Retail, or Manufacturing verticals with complex regulatory data requirements.
• Contributions to open-source data projects or published technical content (blogs, conference talks, white papers).
• Exposure to data mesh principles and federated data ownership models.