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Lead Data Engineer, Czech Republic- Remote (Prague, Prague 5, CZ, 150 000) | WithMira

Resumen del rol

Lead Data Engineer, Czech Republic- Remote (Prague, Prague 5, CZ, 150 000)

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Design and oversee the implementation of an Operational Data Store (ODS) to provide a unified, real-time view of production data.
Build low-latency data streams using technologies like Kafka or Flink to power embedded analytics directly within our customer-facing applications.
Establish "Data Contracts" with upstream engineering teams to ensure high availability and schema stability for all real-time operational flows.

Own the transition and scaling of our Analytical Data Store (e.g., Snowflake), ensuring it is optimized for both performance and cost-efficiency.
Modernize our transformation layer by implementing robust ELT patterns and modular data modeling (using dbt and airflow) to support enterprise-wide
Reporting and building scalable data pipelines that power real-time decision-making across our global enterprise.
Champion Data Governance, ensuring that every dashboard and report is backed by high-quality, audited, and well-documented data.

Build the "Data Foundation" for Machine Learning, including the development of Feature Stores and automated pipelines for model training and inference.
Support AI initiatives by architecting solutions for unstructured data, such as integrating Vector Databases to power LLM-based features.
Collaborate with Analytics teams to bridge the "MLOps" gap, ensuring that models move from notebooks to production seamlessly and reliably.

Mentor and grow a high-performing engineering team, fostering a culture of "DataOps" where automation, testing, and observability are the default.
Manage the Data FinOps strategy, balancing the need for high-performance compute with long-term cloud budget sustainability.
Act as a strategic partner to Product and Executive leadership, translating complex technical roadmaps into clear business value.

Experience: 8+ years in Data Engineering, with at least 3+ years in a formal leadership or management role.
Modern Data Stack (MDS): Proven experience architecting cloud data warehouses (Snowflake, BigQuery, or Databricks).
The "Core Two": Expert-level proficiency in Python (for automation/pipelines) and SQL (for complex modeling and optimization).
Cloud Engineering: Proficiency in AWS infrastructure management and event-driven pipelines (Kinesis, IAM, Monitoring, and IaC frameworks).

Real-Time Infrastructure: Hands-on experience with stream processing tools (Kafka, Flink, or Spark Streaming). You must understand how to move data in sub-second increments for your operational store.
System Design: Ability to design ELT/ETL architectures from scratch using dbt, with a focus on idempotency, scalability, and error handling.
Data Governance: Experience implementing data quality frameworks (e.g., Great Expectations, Monte Carlo) and ensuring compliance (GDPR/CCPA).

Product-Led Engineering: Experience in a "Product-led" organization where engineering is a value-driver
Stakeholder Management: Ability to communicate complex architectural constraints (like latency or data consistency) to non-technical partners in terms of business impact and ROI.
Cross-functional Collaboration: Proven track record of working with Product Managers to ship data-intensive features in an Agile environment.

MLOps Knowledge: Familiarity with the ML lifecycle—specifically building Feature Stores and orchestrating pipelines via Airflow, Dagster, or Prefect.
Vector Database Exposure: Experience with (or deep understanding of) Pinecone, Milvus, or Weaviate for supporting LLM/GenAI initiatives.
Unstructured Data: Experience managing "Bronze" layer data lakes containing JSON, images, or text for AI training.

FinOps Mastery: A track record of significantly reducing cloud warehouse costs through query optimization and compute management.
Software Engineering Background: Previous experience as a Frontend or Backend Engineer.

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