Manage and support a team of 2 to 3 senior Data Engineers, providing regular feedback, coaching, and career development.
Foster a collaborative, high-performing, and accountable team environment.
Ensure strong ownership, clarity of priorities, and high engineering standards.
Drive the reliability, scalability, and evolution of our core data infrastructure (Spark, Kafka, transformation layers).
Define and enforce best practices around data quality, observability, and monitoring.
Ensure the platform remains trusted, stable, and scalable as usage grows.
Lead initiatives to improve performance and optimize costs (FinOps mindset).
Own the evolution of orchestration tools (Airflow) and ensure a smooth developer experience for data consumers.
Define and support the infrastructure strategy for AI and ML use cases.
Enable scalable solutions for ML workflows, model deployment, and LLM integration.
Guide architectural decisions and ensure pragmatic, maintainable solutions.
Participate in design discussions and support complex problem-solving.
Act as a bridge between engineering, data, and product stakeholders.

People Leader: You enjoy growing engineers and building strong, autonomous teams.
Technical Anchor: You have a strong data engineering background and can guide technical decisions.
Pragmatic: You focus on impact, scalability, and maintainability over complexity.
Collaborative: You work effectively across teams and value shared ownership.
Product & User-Oriented: You care about the experience of internal users (Data Scientists, Analysts, Engineers).
Forward-Thinking: You are curious about the evolution of data platforms, AI, and modern data ecosystems.
Core Engineering & Data: Strong experience with Python and SQL
Solid experience with distributed data processing (Spark, Kafka)
Platform & Infrastructure: Experience with workflow orchestration (Airflow)
Familiarity with AWS, Docker, Kubernetes
Experience working with modern data stacks (e.g., Snowflake, dbt)
Leadership: Previous experience managing or mentoring engineers
Ability to drive technical decisions and prioritize effectively
MLOps & AI (plus): Experience with ML pipelines and tools (MLflow, SageMaker)
Exposure to LLM/GenAI infrastructure (e.g., Vector DBs, Bedrock)
Nice-to-Haves: Experience building internal data platforms
Familiarity with Infrastructure as Code (Terraform)
Exposure to FinOps and cost optimization practices

Data Engineering Manager

Key skills