Why this Role Exists

At Blip, data doesn’t just inform decisions, it powers experiences. Every message, transaction, and insight flows through our data ecosystem, and we’re scaling it to handle billions of daily events with sub-second intelligence.

We’re looking for a Staff Data Engineer to shape and evolve the backbone of our real-time data processing layer, someone capable of designing and building data-intensive systems that serve as the foundation for analytics, AI, and intelligent automation at global scale.

Your Mission

You’ll design and implement streaming and batch data systems that move and transform information reliably, efficiently, and transparently across Blip’s platform.

You’ll champion data modeling excellence, ensuring the data we produce is high-quality, discoverable, and ready for real-time consumption — by both humans and machines.

Key Responsibilities

Data Engineering & Pipeline Architecture

• Design, implement, and optimize high-throughput, low-latency data pipelines capable of processing billions of events per day.
• Build scalable data ingestion and transformation frameworks leveraging Kafka, Flink, Spark, Airflow, Beam, and/or GCP related data engineering technologies.
• Ensure fault-tolerance, exactly-once semantics, and replayability in all event processing flows.
• Establish reusable patterns and accelerators for pipeline development, deployment, and monitoring.

Modeling & Storage Design

• Design data models across streaming, analytical, and serving layers (Bronze/Silver/Gold, event sourcing, CDC, and dimensional schemas).
• Partner with Data Architects to evolve domain-driven data models ensuring semantic alignment and traceability from source to consumption.
• Implement optimal partitioning, clustering, and indexing strategies for performance and cost efficiency across Databricks, BigQuery and other storage & processing engines.
  
  

Reliability, Quality & Observability

• Embed observability, lineage, and data quality validations into every pipeline.
• Define and enforce SLAs and SLOs for data delivery, freshness, and accuracy.
• Collaborate with Platform Engineers to integrate metrics, alerts, and self-healing mechanisms for critical data flows.

Collaboration & Leadership

• Act as a technical leader within the Data Engineering team: leading design reviews, conducting architecture spikes, and mentoring engineers.
• Work closely with AI, Platform, and Product teams to deliver event-driven capabilities that power intelligent products in real time.
• Promote craftsmanship, performance, and simplicity as core engineering values.

What We’re Looking For

Experience

• 6+ years in Data Engineering, Software Engineering, or Distributed Systems roles.
• Proven experience building and operating data-intensive and streaming systems in production.
• Hands-on experience with Apache Kafka, Flink, Spark Streaming, Databricks & GCP related data engineering technologies.
• Strong understanding of event-driven architectures, change data capture (CDC), and real-time data modeling patterns.
  
  

Technical Foundations

• Proficiency in Python or PySpark, and solid command of SQL.
• Experience with cloud-native data services (GCP, Azure, or AWS) and workflow orchestration (Airflow/Composer).
• Deep understanding of data modeling and storage formats (Delta, Parquet, Avro).
• Experience with CI/CD for data pipelines, testing frameworks, and infrastructure automation (Terraform).
  
  

Bonus Points

• Experience with event sourcing or CQRS-style systems for transactional and analytical data flows.
• Knowledge of feature store architectures, real-time machine learning serving, or agentic data pipelines.
• Contributions to open-source streaming frameworks or performance optimization initiatives.

Informações adicionais