Job Description

We are looking for a visionary Director of Data Engineering to lead our data engineering, infrastructure, and analytics strategy.

In this role, you will oversee the foundation of our data-driven culture, ensuring that our infrastructure is scalable, secure, and high-performing. You will act as a strategic partner to the executive stakeholders, translating business goals into a robust technical roadmap to drive Rise to the next level of data-driven organization.

Responsibilities

• Strategic Leadership: Direct the data engineering, data infrastructure, and MLOps functions. Define the long-term vision for our data ecosystem, moving toward a decentralized Data Mesh where R&D teams "own" their data products.
• Stakeholder Management: Align data initiatives with global business objectives and provide transparency into the data roadmap.
• Data & Cloud Infrastructure: Oversee the design and maintenance of our core data infrastructure. Drive FinOps initiatives to optimize cloud spend and compute efficiency for high-volume storage.
• Architectural Excellence: Design real-time data architectures and implement Data Contracts to ensure reliability and quality across the entire engineering pipeline.
• Team Development: Lead and mentor a multi-disciplinary team of engineers, fostering a "Product Engineering" mindset and a culture of technical excellence.

Collaboration: Bridge the gap between Software Engineering, Data Science, and Product. Ensure Data Science teams have the high-quality, model-ready data they need to drive innovation.

Requirements

• Data Warehousing: Expert-level knowledge of modern Cloud Data Warehouses (e.g., BigQuery, Snowflake, or Redshift). Proven experience managing petabyte-scale datasets and optimizing for both performance and cost.
• Big Data Processing: Deep hands-on experience with Apache Spark (PySpark/Scala) for large-scale ETL and real-time data enrichment.
• Workflow Orchestration: Proficiency in Apache Airflow or similar tools; ability to design complex, multi-tenant DAGs and manage production-grade orchestration.
• Streaming & Event-Driven Systems: Experience with high-throughput ingestion and messaging systems (e.g., Kafka, Pub/Sub).
• Infrastructure & DevOps: Strong understanding of cloud infrastructure (GCP/AWS) and experience running production systems at scale.

AI/ML Foundations: Familiarity with MLOps and the infrastructure required to support AI applications (e.g., Vector Databases or model deployment pipelines).

• 4+ years of experience in a leadership role within Data Engineering or high-scale infrastructure environments.
• 6+ years in Data Engineering, Big Data, or Infrastructure roles.
• Leadership: Proven experience in a management or lead role, with a track record of scaling teams and infrastructure in high-growth environments.
• Experience in high-scale, high-throughput environments (e.g., real-time bidding, adtech, or high-volume log processing) is a significant advantage.
• Strong understanding of GCP/AWS cloud infrastructure and cloud cost management.
• Passion for business results and product orientation with experience in building the technical requirements for internal products.

Excellent problem-solving skills and the ability to make data-driven decisions.

Advantages

• Experience with data engineering of an adTech company or any other experience in the adTech industry
• Experience in creation of requirement document for Data Engineering teams
• Successful building and driving adoption of internal tools
• Experience with decision supporting systems, and automation of decisions.
• 
  
• Experience with GenAI data technologies, data processing and optimization, e.g. optimizing vector DBs.