Director Data Engineer

As Director, you will lead a high-performing team responsible for building and operating scalable data platform components within the company's Unified Data Platform. You will drive execution, ensure engineering excellence, and contribute to the evolution of a cloud-native, AI-ready data ecosystem serving multiple countries.

Key Responsibilities

Lead and grow a team of engineers to deliver scalable, reliable data platform capabilities

Own end-to-end delivery of platform components (design build run) with clear accountability for quality, performance, and reliability

Partner with Product Managers to translate business needs into scalable technical solutions

Contribute to and implement target data architecture aligned with enterprise standards

Drive best practices in data engineering, DevOps, and platform reliability

Ensure systems are built with observability, cost efficiency, and scalability in mind

Collaborate with domain teams and region specific (LOB) teams to enable reusable data products and standardized platform capabilities

Identify and resolve systemic issues, focusing on long-term engineering health

Evaluate new technologies and lead proof-of-concepts where required

Build strong engineering culture with focus on ownership, accountability, and continuous improvement

Data & Technical Expertise

14+ years of overall experience with 8+ years in data engineering, data platforms, or distributed data systems

Strong hands-on experience with Google Cloud Platform (GCP), especially:

BigQuery (data warehousing, performance tuning, cost optimization, partitioning/clustering)

Dataflow (Apache Beam) for large-scale batch and streaming pipelines

Pub/Sub for real-time ingestion and event-driven architectures

Cloud Composer (Airflow) for orchestration and workflow management

GCS (Cloud Storage) as part of lakehouse or staging architectures

Deep understanding of modern data architectures including Lakehouse patterns on GCP and distributed processing systems

Strong experience designing and operating end-to-end data pipelines (ingestion to transformation to serving) at scale

Expertise in real-time and streaming architectures, including event design, schema evolution, and fault-tolerant processing

Hands-on programming skills in Python, Spark (Scala), with experience in building distributed data applications

Experience implementing CI/CD for data pipelines, including versioning, testing, and deployment automation

Strong understanding of data modelling and optimization for analytical workloads in BigQuery

Practical exposure to data product thinking, including:

Data contracts and schema governance

Discoverability and reuse across domains

Ownership and lifecycle management

Familiarity with MLOps and AI-enabled data platforms on GCP, including support for:

Feature engineering pipelines

Model training and inference workflows

Integration with Vertex AI (preferred)

Strong focus on platform reliability and observability, including monitoring, alerting, lineage, and data quality frameworks

Experience managing cost-performance trade-offs in GCP (e.g., BigQuery cost controls, Dataflow optimization)

Proven ability to work in globally distributed, federated environments, enabling standardization across multiple teams and geographies

Awareness of evolving trends in cloud-native data platforms, data mesh, and event-driven architectures, with the ability to apply them pragmatically

Leadership & Collaboration

Proven ability to lead and grow high-performing engineering teams with a strong focus on ownership, accountability, and continuous improvement

Ability to operate effectively in a federated, multi-country environment, influencing teams without direct authority

Strong stakeholder management skills, with experience collaborating across product, architecture, and business teams

Clear and concise communication skills, with the ability to articulate complex technical concepts to senior leadership

Experience fostering a product and platform mindset within engineering teams.