About the Role

We are looking for a hands-on Lead Data Engineer to drive the technical evolution of our data platform. This role combines approximately 50% hands-on engineering with 50% architecture, technical leadership, and mentorship.

You will be responsible for shaping a high-throughput data infrastructure, building production-grade pipelines and data assets, improving platform scalability and reliability, and helping a growing engineering team raise its technical standards.

Key Responsibilities

1. Data Platform Architecture & Scalability

• Define and drive the technical architecture of the company‘s streaming and batch data infrastructure as data volumes and platform requirements continue to scale.
• Identify and resolve performance bottlenecks across data storage, processing, and querying layers.
• Optimize data models, query execution, and storage strategies to achieve the right balance between performance, scalability, and infrastructure costs.
• Establish architectural standards and technical direction for the broader data platform.

2. Data Pipeline & Identity Engineering

• Design and implement reliable, high-throughput ETL pipelines using the Medallion architecture to transform raw data into trusted, analytics-ready datasets.
• Build fault-tolerant data processing workflows capable of handling large-scale data volumes.
• Design and evolve identity graph capabilities that consolidate fragmented consumer attributes, visitor information, leads, and customer identities into a unified identity layer.
• Ensure core data products remain reliable, scalable, and fit for downstream analytics and product use cases.

3. ML Data Engineering & MLOps

• Develop and maintain feature pipelines and feature stores that support production AI/ML models, including use cases such as identity resolution and propensity scoring.
• Work closely with Data Science teams to productionize models and integrate them into scalable data workflows.
• Support model deployment, infrastructure scaling, monitoring, and retraining processes.
• Ensure data pipelines provide reliable and production-ready inputs for ML systems.

4. Data Governance & Engineering Excellence

• Establish data governance standards covering data ownership, lineage, cataloging, access control, and compliance.
• Implement frameworks and best practices for data quality, pipeline testing, CI/CD, and observability.
• Improve the reliability and maintainability of the company‘s data infrastructure through automation and engineering best practices.
• Mentor junior and mid-level engineers and contribute to a strong culture of technical ownership and continuous improvement.

Requirements

• 8+ years of professional experience in Data Engineering, with demonstrated experience leading technical initiatives or mentoring engineers.
• Strong programming skills in Python; experience with Node.js is a plus.
• Advanced SQL skills and strong expertise in data modeling for large-scale analytical environments.
• Extensive production experience with cloud-based data platforms, including AWS (S3, Kinesis, Athena, Redshift, Lambda) or equivalent GCP services such as BigQuery, Dataflow, Pub/Sub, and Cloud Storage.
• Hands-on experience with ClickHouse or another large-scale columnar/OLAP database.
• Strong experience designing and operating data workflows with Airflow or similar orchestration frameworks.
• Solid understanding of Lakehouse architecture and hands-on experience implementing Medallion data layering.
• Experience developing data pipelines that support ML/AI use cases, including feature pipelines or feature stores.
• Strong English communication skills and the ability to collaborate effectively with distributed, multi-country engineering teams.

Nice to Have

• Background in MarTech/AdTech, particularly identity resolution, first-party cookie data, or digital marketing platforms.
• Exposure to Data Science / MLOps technologies and frameworks.
• Experience working in an early-stage or rapidly scaling B2B SaaS environment with a strong ownership culture.