Team Lead, Data Engineering

Duties & Responsibilities

• Technical Leadership: Direct, mentor, and inspire a world-class team of data engineers to deliver robust enterprise-grade data platforms and pipelines. Establish and enforce advanced engineering best practices, frameworks, and coding standards.
• Architecture & Design: Architect scalable, high-performance, and resilient data solutions for structured and unstructured data across on-premises, hybrid, and multi-cloud environments. Oversee data modeling, data warehousing, and metadata management initiatives.
• Engineering Excellence: Lead the implementation of CI/CD pipelines, infrastructure-as-code, containerization (e.g., Docker, Kubernetes), automated testing, and monitoring to enable rapid, secure, and reliable deployment cycles.
• Data Security & Compliance: Ensure all solutions adhere to global data privacy regulations (e.g., GDPR, CCPA), internal security frameworks, and bank policies. Drive data lineage, governance, auditing, and data loss prevention (DLP) strategies at every layer.
• Stakeholder Management: Collaborate with data architects, analytics leaders, business stakeholders, and product owners to translate complex requirements into scalable technical deliverables with clear acceptance criteria.
• Operational Resilience: Oversee production support, real-time monitoring, incident response, disaster recovery, and root cause analysis. Proactively resolve incidents and implement robust corrective and preventive actions.
• Project & Resource Management: Manage project delivery, team resourcing, budgeting, and risk prioritization to meet aggressive timelines and service-level agreements (SLAs).
• Continuous Improvement: Cultivate a culture of ongoing technical innovation, automation, and continuous learning to maintain a competitive edge in data engineering capabilities and technology trends.

Job Requirements

Education

• Minimum Bachelor's degree in Computer Science, Engineering, Data Science, or a related field.
• Master's degree or relevant certifications (e.g., Google Professional Data Engineer, AWS Certified Data Analytics) strongly preferred.

Experience

• 8+ years of progressive experience in data engineering, data architecture, or software engineering, with at least 2 years in a technical or people leadership role within complex, regulated environments.
• Proven expertise in designing and deploying large-scale data platforms using modern data stack technologies (e.g., Spark, Kafka, Flink, Hadoop, Snowflake, Databricks, Airflow), RDBMS/NoSQL databases, and real-time/streaming architectures.
• Deep hands-on skills in Python, Java, Scala, or a similar advanced programming language; experience with API design, microservices, and RESTful architectures.
• Comprehensive knowledge of CI/CD, DevOps, infrastructure-as-code (e.g., Terraform, Ansible), and container orchestration technologies.
• Strong understanding of data security, encryption, access controls, and regulatory frameworks relevant to global banking.
• Track record of innovation, process automation, and driving operational excellence.