Senior Data Engineer
Skills
SQLAirflowAmazon Web ServicesApache FlinkApache KafkaApache SparkData Build Tool
What the job involves
The main requirements, responsibilities and hiring steps.
Requirements
- Strong data engineering fundamentals including data modeling partitioning performance tuning and cost-aware design for high-volume workloads
- Experience building streaming and event-driven systems with Kafka or queues including late and out-of-order events backfills and other data edge cases
- Strong SQL and Python skills with modern data stack tooling such as Spark Airflow Dagster dbt and warehouse or lakehouse patterns
- Hands-on AWS experience operating data systems with monitoring incident response and security considerations including PII access control encryption and auditability
- Familiarity integrating data with AI ML and agentic systems including feature pipelines evaluation datasets grounding inputs and feedback capture from agent outcomes
Nice to have
- Pragmatic
- Clean code
- Async collaboration
- Low-ego
- Quick learner
- Clear communicator
Day to day
- Design and maintain high-volume data pipelines for batch and streaming workloads that power agentic AI features and core product workflows.
- Build event-driven components with Kafka and message queues, applying idempotency replay strategies and backfill mechanisms to handle real-world data edge cases.
- Develop data models and transformation layers using lakehouse and dbt-style patterns while ensuring strong data quality reliability and operational support.
- Enable AI and ML workflows with robust datasets for training evaluation feature generation and feedback loops from production agents.
Hiring process
- CV & Profile Review – Relevant experience and background
- Initial Chat – Motivation and role fit
- Architecture Interview – Architecture design choices and real scenarios
- Agentic Coding Exercise – Small task executed by agents