Head of Data Engineering

Description

The Head of Data Engineering is a strategic and technical leader responsible for building, scaling, and evolving our enterprise data engineering capabilities. This role oversees the design and implementation of modern data pipelines, ensures data infrastructure resilience, and enables analytics, AI / ML, and business intelligence across a multi-platform, multi-cloud ecosystem.

With deep expertise in big data platforms and a strong people leadership mindset, the Head of Data Engineering will lead a high-performing team, define architectural standards, drive value stream improvements with automation, and ensure alignment with enterprise data governance and security policies. This position will also serve as a key advisor to senior leadership for delivering business-impacting data solutions.

Data Engineering Strategy & Execution :

Define and execute the data engineering vision, roadmap, and technology stack aligned with business priorities and enterprise architecture.

Champion modern data practices, including modular data pipelines, real-time streaming, and cloud-native architectures.

Serve as the primary point of contact for senior leaders regarding solution design and delivery of key data engineering initiatives.

Ensure alignment with data governance, privacy, and compliance policies, partnering closely with data stewards and governance teams.

Platform and Architecture Ownership :

Architect and maintain scalable, high-performance, and cost-optimized data infrastructure using modern data platforms such as Databricks, Snowflake, Iceberg, Kafka, Airflow , and dbt.

Guide the modernization and consolidation of legacy systems into a unified data platform.

Collaborate on integration patterns, APIs, and data contracts to ensure seamless interoperability across the ecosystem.

Data Pipeline and Data Product Delivery :

Lead the design, development, and maintenance of secure, automated, and reliable data pipelines across batch and real-time use cases.

Ensure high availability, resiliency, and performance of data processing workflows.

Identifying bottlenecks and optimizing data processing and storage systems for improved performance and efficiency

Embed quality, observability, and lineage into pipeline design to support trust and traceability of data products.

Technology Evaluation and Adoption :

Stay current with emerging technologies, architecture patterns, and open-source innovations.

Lead the assessment and adoption of new tools that enhance delivery velocity, platform reliability, and cost-efficiency.

Team Leadership and Capability Development :

Build, mentor, and lead a high-caliber team of data engineers and platform specialists.

Foster a culture of ownership, continuous improvement, psychological safety, and technical excellence.

Develop career paths, capability uplift programs, and peer-learning activities across the team.

Cross-functional Collaboration :

Collaborate with analytics, data science, product, IT, and business units to understand data requirements and co-create scalable solutions.

Act as a bridge between engineering and non-technical stakeholders to translate needs into actionable data architecture and solutions.

Compliance and Security

Ensuring compliance with data privacy regulations and implementing robust security measures to protect sensitive data from unauthorized access or breaches.

Experience :

8–10 years of leadership experience, with a proven track record of managing high-performing data engineering teams.

8+ years of hands-on experience in building and supporting ETL / ELT pipelines and data platforms at scale.

5+ years of experience in Java (or equivalent backend language).

Strong background in data architecture, distributed systems, and cloud-native platforms.

Demonstrated experience in implementing operational and analytical data stores, real-time streaming systems, and modern data lakes, lakehouses and warehouses.

Exposure to enterprise leadership and experience delivering technical solutions aligned to business priorities.

Skills :

In-depth knowledge of Tier 1 Data Engineering tools is preferred, dbt Core, Apache Spark, NiFi, Kafka, Airflow, and SQL.

Solid understanding of data pipeline orchestration, data quality, security, and performance optimization.

Familiarity with SDLC and DevSecOps methodologies including Agile, CI / CD, and automated testing.

Strong business acumen and the ability to align technical strategy with enterprise value delivery.

Working knowledge of data governance and master data management practices.

Education :

Bachelor’s degree in Computer Science, Engineering, or a related field. Master’s degree preferred.

What Success Looks Like

Scaled and reliable data platform enabling business agility and insights.

Reduced time to insight (lead time) and cycle times for data product delivery through automation and reusability.

High team engagement and strong talent retention within the Data Engineering organization.

Measurable improvements in data quality, pipeline observability, and platform cost-efficiency.

Strong stakeholder confidence in data engineering’s ability to deliver strategic value.

Equal Opportunity Employer

Globe’s hiring process promotes equal opportunity to applicants, Any form of discrimination is not tolerated throughout the entire employee lifecycle, including the hiring process such as in posting vacancies, selecting, and interviewing applicants.

Globe’s Diversity, Equity and Inclusion Policy Commitment can be accessed

Make Your Passion Part of Your Profession. Attracting the best and brightest Talents is pivotal to our success. If you are ready to share our purpose of Creating a Globe of Good, explore opportunities with us.