Senior Backend Engineer, Data Platform
New
L
LaunchDarklySoftware Development
Remote - USFull-TimeSenior
SalaryZone 1: $187,000 - $257,000; Zone 2: $168,000 - $231,000; Zone 3: $158,000 - $218,000
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Job Details
- Experience
- 6+ years
- Required Skills
- PostgreSQLPythonSQLAirflowApache KafkaGoSparkTerraformDatadogDistributed Systems
Requirements
- 6+ years of professional backend software engineering experience, including significant experience with infrastructure, data platforms, or distributed systems.
- Experience designing, building, operating, and debugging reliable production systems that move, store, process, or query large volumes of data.
- Hands-on experience with data pipeline, streaming, orchestration, or batch-processing technologies (e.g., Kinesis, Airflow, Spark, Lambda, Flink, Athena, Kafka).
- Experience working with analytical, event, warehouse, or operational data stores (e.g., ClickHouse, Postgres, Elasticsearch, Timestream, Glue/Iceberg/S3, Redshift, Databricks).
- Strong programming experience in Go, Python, SQL, Scala, or similar backend languages.
- Familiarity with distributed systems fundamentals such as concurrency, data modeling, failure handling, retries, idempotency, and high-throughput processing.
- Experience with infrastructure-as-code tools (e.g., Terraform).
- Experience with observability tools (e.g., Datadog, New Relic).
- Strong ownership mindset for production systems, including maintainability, testing, and operational excellence.
Responsibilities
- Design, build, and operate distributed, data-intensive backend systems that power Data Platform capabilities.
- Own and improve production data infrastructure, including streaming and batch pipelines, analytical data stores, and warehouse export systems.
- Debug and resolve complex production issues across data pipelines, databases, and cloud infrastructure.
- Participate in the team’s on-call rotation to support production systems.
- Partner with product managers, frontend engineers, and UX designers to deliver reliable customer-facing data capabilities.
- Write and review technical proposals, contribute to architecture decisions, and help make trade-offs regarding scalability and reliability.
- Improve engineering standards, testing practices, and operational processes for Data Platform systems.
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