Overview
Role: Staff Data Engineer at Blip. You will shape and evolve the real-time data processing layer and build data-intensive systems that support analytics, AI, and intelligent automation at global scale.
Responsibilities
* Data Engineering & Pipeline Architecture
o Design, implement, and optimize high-throughput, low-latency data pipelines capable of processing billions of events per day.
o Build scalable data ingestion and transformation frameworks leveraging Kafka, Flink, Spark, Airflow, Beam, and/or GCP data engineering technologies.
o Ensure fault-tolerance, exactly-once semantics, and replayability in all event processing flows.
o Establish reusable patterns and accelerators for pipeline development, deployment, and monitoring.
* Modeling & Storage Design
o Design data models across streaming, analytical, and serving layers (Bronze/Silver/Gold, event sourcing, CDC, and dimensional schemas).
o Partner with Data Architects to evolve domain-driven data models ensuring semantic alignment and traceability from source to consumption.
o Implement optimal partitioning, clustering, and indexing strategies for performance and cost efficiency across Databricks, BigQuery and other storage & processing engines.
* Reliability, Quality & Observability
o Embed observability, lineage, and data quality validations into every pipeline.
o Define and enforce SLAs and SLOs for data delivery, freshness, and accuracy.
o Collaborate with Platform Engineers to integrate metrics, alerts, and self-healing mechanisms for critical data flows.
* Collaboration & Leadership
o Act as a technical leader within the Data Engineering team: lead design reviews, conduct architecture spikes, and mentor engineers.
o Work closely with AI, Platform, and Product teams to deliver event-driven capabilities that power intelligent products in real time.
o Promote craftsmanship, performance, and simplicity as core engineering values.
What We’re Looking For
* Experience
o 6+ years in Data Engineering, Software Engineering, or Distributed Systems roles.
o Proven experience building and operating data-intensive and streaming systems in production.
o Hands-on experience with Apache Kafka, Flink, Spark Streaming, Databricks & GCP related data engineering technologies.
o Strong understanding of event-driven architectures, change data capture (CDC), and real-time data modeling patterns.
* Technical Foundations
o Proficiency in Python or PySpark, and solid command of SQL.
o Experience with cloud-native data services (GCP, Azure, or AWS) and workflow orchestration (Airflow/Composer).
o Deep understanding of data modeling and storage formats (Delta, Parquet, Avro).
o Experience with CI/CD for data pipelines, testing frameworks, and infrastructure automation (Terraform).
* Bonus Points
o Experience with event sourcing or CQRS-style systems for transactional and analytical data flows.
o Knowledge of feature store architectures, real-time machine learning serving, or agentic data pipelines.
o Contributions to open-source streaming frameworks or performance optimization initiatives.
Benefits
* Advantages and benefits information may be country-specific. This section outlines typical categories such as flexible hours, remote/hybrid/work-from-office options, no dress code, home office support, birthday day off, paid time off, and recharge days.
* Well-being and quality of life: meal/voucher benefits, health plans, gym/wellness programs, and cultural/lifestyle benefits.
* Family, parental leave, and caregiver support: extended maternity/paternity leave, childcare assistance, and dependent support.
* Development and recognition: educational incentives and short-term incentive programs tied to company goals.
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