Lead Data Engineer

This is a fully remote position, open to applicants in Czechia.

📋 Description

• Design and lead the implementation of an Operational Data Store (ODS) by architecting and managing operational data flows.

• Develop low-latency data streams utilizing technologies such as Kafka or Flink to enable embedded analytics in customer-facing applications.

• Create "Data Contracts" with upstream engineering teams to ensure schema stability and high availability for all real-time operational data flows.

• Oversee the transition and scaling of our Analytical Data Store (e.g., Snowflake), optimizing it for both performance and cost-effectiveness.

• Enhance the transformation layer by applying robust ELT patterns and modular data modeling techniques (using dbt and airflow).

• Advocate for Data Governance, ensuring all dashboards and reports are supported by high-quality, well-audited, and documented data.

• Establish the "Data Foundation" for Machine Learning, including the creation of Feature Stores and automated pipelines for model training and inference.

• Mentor and develop a high-performing engineering team, cultivating a "DataOps" culture where automation, testing, and observability are standard practices.

• Serve as a strategic partner to Product and Executive leadership, translating intricate technical roadmaps into tangible business value.

⛳️ Requirements

• Over 8 years of experience in Data Engineering, including a minimum of 3 years in a formal leadership or management capacity.

• Demonstrated experience in architecting cloud data warehouses (Snowflake, BigQuery, or Databricks).

• Advanced proficiency in Python (for automation/pipelines) and SQL (for complex modeling and optimization).

• Expertise in managing AWS infrastructure and event-driven pipelines (Kinesis, IAM, Monitoring, and IaC frameworks).

• Practical experience with stream processing tools (Kafka, Flink, or Spark Streaming).

• Capability to design ELT/ETL architectures from the ground up using dbt, emphasizing idempotency, scalability, and error handling.

• Experience in implementing data quality frameworks (e.g., Great Expectations, Monte Carlo) and ensuring regulatory compliance (GDPR/CCPA).

• Background in a "Product-led" organization where engineering is a significant value contributor.

• Ability to articulate complex architectural constraints (such as latency or data consistency) to non-technical stakeholders in terms of business impact and ROI.

• Proven history of collaborating with Product Managers to deliver data-intensive features in an Agile environment.

🏝️ Benefits

• Remote-First operating model and culture.

• Collaboration spaces designed for team members to work together in person.