System Analyst

Job Purpose | The BFL Data Technologies team requires a hands-on Lead Data Architect / Data Engineering leader to design, modernize, and govern scalable enterprise data platforms across PostgreSQL, Azure, Databricks, Delta Lake, and cloud-native data services. The role will lead a team of data engineers and database specialists, own solution architecture and delivery quality and automation practices, and ensure reliable, secure, cost-optimized, and data products for business and analytics stakeholders. | Duties and Responsibilities | Data Architecture, Strategy and Delivery Leadership • Lead the design and delivery of enterprise-grade data solutions across PostgreSQL, Azure Data Services, Azure Databricks, Delta Lake, Data Lakehouse, and related cloud data platforms. • Translate business, product, analytics, regulatory, and operational requirements into scalable data architecture patterns, technical roadmaps, and delivery plans. • Define standards for data modelling, database design, lakehouse layering, data contracts, coding practices, reusable frameworks, and solution documentation. • Own architecture reviews, design governance, technical risk assessment, delivery health, and stakeholder communication for critical data initiatives. Hands-on Data Engineering and Database Development • Design, build, review, and optimize batch, micro-batch, and near-real-time ETL/ELT pipelines using Azure Data Factory, Databricks, Spark, SQL, Python, and orchestration tools. • Provide deep PostgreSQL expertise covering schema design, indexing, query optimization, stored procedures, partitioning, execution plan analysis, replication patterns, and performance tuning. • Implement Lakehouse design patterns such as bronze, silver, and gold layers, Delta tables, schema evolution, incremental processing, change data capture, and reusable curated data products. • Drive engineering excellence through peer reviews, automated testing, CI/CD pipelines, environment promotion, release controls, and version management using Azure DevOps or GitHub. People Leadership and Stakeholder Partnership • Lead, mentor, and develop a team of data engineers, database developers, and platform specialists through technical coaching, design walkthroughs, code reviews, and capability-building plans. • Partner with business, product, analytics, risk, security, infrastructure, and application teams to align data solutions with enterprise priorities and measurable business outcomes. • Communicate architecture decisions, delivery progress, dependencies, risks, and trade-offs clearly to senior stakeholders and cross-functional teams. • Promote a culture of ownership, documentation, automation, quality, continuous improvement, and responsible use of data for analytics, AI, and operational use cases. | Required Qualifications and Experience | Qualifications: Bachelor’s or Master’s degree in Computer Science, Engineering, Information Technology, Data Engineering, Mathematics, Statistics, or a related discipline. Relevant certifications in Azure, Databricks, PostgreSQL, data architecture, cloud data engineering, or data governance will be an added advantage. Work Experience: 6 to 9 years of progressive experience in database engineering, data architecture, cloud data platforms, or enterprise data engineering, including hands-on delivery experience and at least 3 years of leading, mentoring, or technically governing engineering teams in BFSI, fintech, or large-scale technology environments. Skills Looking for: • Strong hands-on expertise in PostgreSQL, including database design, SQL development, indexing, query tuning, partitioning, performance troubleshooting, and high-volume transactional or analytical workloads. • Deep working knowledge of Azure data ecosystem, including Azure Data Factory, Azure Data Lake Storage Gen2, Azure Storage, Azure SQL, Azure Synapse or Fabric concepts, Key Vault, and cloud-native security patterns. • Strong experience with Azure Databricks, Apache Spark, PySpark, SQL, Delta Lake, medallion architecture, job orchestration, workload tuning, and scalable lakehouse implementation. • Ability to design and govern enterprise data models, canonical data definitions, data contracts, metadata standards, reusable patterns, and domain-oriented data products. • Experience with ETL/ELT design, CDC, batch and streaming integration, API-based ingestion, orchestration, dependency management, and pipeline recovery patterns. • Exposure to NoSQL and distributed data stores such as Cosmos DB, MongoDB, Cassandra, Redis, or similar technologies for appropriate use cases. • Strong DataOps mindset with experience in Git, Azure DevOps or GitHub, CI/CD, automated testing, code quality checks, release management, and infrastructure-as-code concepts. |