Job Summary
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: Bachelors or Masters 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.
