Senior Scalability Engineer
J
Judi HealthHealth technology
Remote, US Salary RangeFull-TimeSenior
Salary110,400 - 213,000 USD per year
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Job Details
- Experience
- 10+ years
- Required Skills
- AWSPostgreSQLPythonKafkaSnowflakeRedshiftDistributed Systems
Requirements
- 10+ years of software engineering experience with demonstrated progression into technical leadership roles.
- 3+ years of experience leading technical initiatives, architecting distributed systems, or serving as a subject matter expert on streaming infrastructure.
- Strong expertise in Python (Flask/SQLAlchemy) for production applications.
- Deep PostgreSQL knowledge: Understanding of write-ahead logs, replication, logical decoding, and change data capture mechanisms.
- Production streaming experience: Proven track record building and operating high-throughput streaming systems using Kinesis, Kafka, or similar event streaming platforms.
- Distributed systems expertise: Strong understanding of ordering guarantees, exactly-once semantics, partition strategies, backpressure handling, and fault tolerance patterns.
- AWS experience: Production experience with Kinesis, S3, SNS/SQS, Lambda, ECS, and data pipeline orchestration.
- Data warehouse knowledge: Experience loading data into Redshift, Snowflake, or similar analytical databases.
- Systems thinking: Ability to design resilient, observable streaming architectures that balance throughput, latency, and reliability.
Responsibilities
- Own streaming infrastructure: Design, implement, and expand WAL-based replication systems that process database changes through Kinesis to Snowflake and Redshift.
- Build CDC systems: Architect and implement change data capture infrastructure for cross-platform data synchronization.
- Develop shared libraries: Create reusable Kinesis/SNS consumer patterns and libraries used across multiple teams.
- Partner with product teams: Work directly with teams to design and implement realtime data processing solutions.
- Ensure data reliability: Implement exactly-once processing semantics, dead letter queues, retry strategies, and monitoring.
- Build observability: Develop monitoring, alerting, and dashboards for streaming pipelines using the LGTM stack.
- Demonstrate technical leadership: Mentor engineers on streaming architecture patterns and lead design reviews.
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