Senior Data Platform Engineer

Swish Analytics•San Francisco, CA

43d•$180,000•Remote

About The Position

The Swish Analytics team is seeking a Senior Data Platform Engineer to have a direct impact on the data infrastructure of our core consumer and enterprise data offerings. We’re a team passionate about accurate predictions and real-time data, and hope you find satisfaction in building new products with the latest and greatest technologies. This is a remote position. Seniority: Solid Senior level (5-8 years experience)

Requirements

Proficiency in Python: Expertise in writing scalable, efficient, and testable code using Python for data processing, automation, and building back-end components. Familiarity with popular Python libraries is a plus.
Cloud Platforms: Proficiency in cloud services such as Amazon Web Services (AWS), Google Cloud Platform (GCP), or Microsoft Azure, including knowledge of their specific data tools (e.g., Redshift, S3, Azure Data Factory, BigQuery).
Operational Excellence: Monitoring (CloudWatch, Performance Insights, Datadog), alerting, on-call experience
MySQL Operations: Deep knowledge of RDS operational functions, performance tuning (indexes, query optimization, explain plans) Backup/restore, point-in-time recovery, replication troubleshooting
SQL Mastery: Proficiently writing and understanding complex SQL queries, including joins, subqueries, aggregations, and window functions.
Database Design Principles: Knowledge of normalization, denormalization, and how table structure impacts query performance.
Indexing: Understanding different index types (B-tree, hash, clustered, non-clustered) and their appropriate use for optimizing search and retrieval.
Automation: Software development for operational tasks
AWS: RDS operations, Authentication, Authorization
Incident Management: Root cause analysis, postmortem creation, Run book development

Nice To Haves

Performance testing tools (sysbench, HammerDB)
Execution Plan Analysis: Ability to read and interpret query execution plans to identify bottlenecks, such as full table scans, inefficient joins, or missing indexes.
Identifying Resource Bottlenecks: Pinpointing where queries are consuming excessive CPU, I/O, connections, or memory.

Responsibilities

24/7 monitoring, alerting, and incident response of enterprise database architecture.
Developing KPI's, SLA’s, SLO’s and strategy to achieve and improve database stability
Backup/recovery testing and disaster recovery planning
Developing performance testing strategy,
Query performance analysis and optimization.
Implement and maintain RDS Proxy and connection management strategies
Manage database environments from production, data streams, to "Big Data" analytics.
Create and maintain policy runbooks and documentation for company use.
Database security, access control, and compliance
Working with MySQL/Postgres, Redshift, Kafka, Athena, Redis/Valkey, S3 and similar
Own schema migration tooling and process development