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Backend Engineer, Data Platform

LaunchDarkly, Inc.

Job Overview

Location

Indiana, USA

Job Type

Full-time

Full Job Description

📋 Description

• Own the backbone of LaunchDarkly’s data universe. You will architect, build, and scale the batch and stream-processing systems that ingest, transform, and deliver trillions of events every single day—turning raw telemetry into the high-value data products that power every major internal dashboard, customer-facing analytics screen, and ML model across the company.
• Design and evolve resilient data pipelines in both batch (Spark on EMR, dbt, Airflow) and real-time (Kafka, Flink, Kinesis) paradigms, ensuring sub-second freshness for streaming use cases and cost-efficient, reproducible runs for daily and hourly batch jobs.
• Optimize storage and compute across AWS (S3, Redshift, DynamoDB, Glue) to balance performance, cost, and durability; you will profile query plans, tune file formats (Parquet/ORC), and implement intelligent partitioning and compaction strategies that keep petabyte-scale datasets performant.
• Build self-service tooling that lets product teams declare their data needs in code (Terraform, YAML) and automatically receive governed, documented, and quality-checked datasets—reducing time-to-insight from weeks to minutes.
• Establish and enforce data-contract standards, schema-evolution policies, and SLAs that guarantee backward compatibility and 99.9 % uptime for mission-critical pipelines relied on by customers in 100+ countries.
• Partner with Security & Compliance to implement end-to-end encryption, PII tokenization, fine-grained IAM, and audit logging that satisfy SOC 2, GDPR, and HIPAA requirements without slowing down delivery velocity.
• Instrument pipelines with real-time observability (Prometheus, Grafana, Datadog) and automated anomaly detection; you will define SLOs, page on them, and lead blameless post-mortems that turn incidents into durable improvements.
• Collaborate cross-functionally with Product Managers, Data Scientists, and Frontend teams to translate fuzzy business questions into precise data models, ensuring that every experiment flag, feature rollout, and customer metric is measurable and trustworthy.
• Contribute to the open-source community by upstreaming fixes to Kafka, Flink, or dbt, and by publishing blog posts and conference talks that share LaunchDarkly’s learnings at scale.
• Mentor junior engineers through pair programming, design reviews, and career-path guidance, fostering a culture where knowledge is shared freely and everyone levels up together.
• Continuously experiment with emerging technologies—Iceberg, Delta Lake, Snowflake, or Rust-based stream processors—to keep the platform two steps ahead of growth and to delight internal customers with ever-faster, ever-cheaper insights.

🎯 Requirements

• 5+ years of backend or data-engineering experience building distributed, high-throughput systems in production (Java, Scala, Kotlin, Go, or Python).
• Expert-level proficiency with at least one major stream-processing framework (Kafka Streams, Flink, or Spark Structured Streaming) and one batch stack (Spark, dbt, Airflow).
• Deep understanding of columnar file formats, partitioning strategies, and cloud data warehouses (Redshift, Snowflake, or BigQuery).
• Nice-to-have: hands-on experience with infrastructure-as-code (Terraform, CDK) and container orchestration (Kubernetes, ECS).

🏖️ Benefits

• Fully remote-first culture with flexible hours and quarterly in-person off-sites in amazing locations.
• Competitive salary + equity + annual learning stipend ($3,000) for conferences, courses, or books.
• 100 % covered medical, dental, and vision insurance for you and 75 % for dependents.
• 20 days PTO, 12 company holidays, and a 4-week paid sabbatical after 4 years.

Skills & Technologies

Backend

Remote

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LaunchDarkly, Inc.

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About LaunchDarkly, Inc.

LaunchDarkly provides a cloud-native feature management platform that enables engineering teams to release, monitor, and control software functionality through feature flags. The platform decouples deployment from release, allowing safe rollouts, targeted experiments, and instant rollbacks without redeploying code. It serves enterprises needing continuous delivery, compliance controls, and multichannel coordination across web, mobile, and edge environments.

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