Senior Data Engineer — Streaming Platform

Hi, we’re Gravie. Our mission is to create health benefits that actually benefit small and midsize businesses and their employees. Our innovative benefit solutions and services are developed and delivered by a diverse group of unique people. We encourage you to be your authentic self - we like you that way.

We’re building a near real-time streaming data platform on Kafka and Flink that turns operational data from across the business into canonical, contract-validated models, serving both operational applications and agentic workloads. We seek a Senior Data Engineer to build, extend, and operate it, owning the platform in production against its latency and throughput SLOs. This is a hands-on role with meaningful ownership of a live production platform.

You are self-driven, calm under production pressure, and comfortable owning streaming systems in a regulated environment.

You Will:

Share full-lifecycle ownership of the streaming platform - architecture, implementation, production operations.

• Run the platform in production: own latency/throughput SLOs, monitoring and alerting (e.g. Datadog) - including replays, per-source backfills, connector-failure recovery, Flink checkpoint/savepoint recovery, dead-letter triage, and tuning for spiky load from batch-driven upstream sources.

• Build and extend streaming pipelines that ingest CDC events from OLTP databases and SaaS sources into canonical, contract-validated form, and define standards to govern changes to them over time.

• Transform and enrich in Apache Flink, including stateful processing and cross-stream joins that correlate events into unified lifecycle entities, with event-time handling for late and out-of-order data.

• Enable secure, governed data access over PHI: classification and row/column controls applied as data is served to consumers, in partnership with Security and Compliance.

• Make it reliable and observable: idempotent/replayable pipeline design, data-quality validation, runbooks, and observability the broader data team can rely on.

• Provision as code: define the platform (streaming, processing, storage, and catalog services on AWS) in CDK with CI/CD for data pipelines, and right-size for cost against the latency SLO.

• Partner across teams: work with upstream producers on source changes and contracts, with downstream consumers on access and data needs, and with stakeholders to turn requirements into what the platform delivers.

• Demonstrate commitment to our core competencies of being authentic, curious, creative, empathetic and outcome oriented.

You Bring:

• 6+ years building and operating production data systems, including demonstrated ownership of streaming or event-driven pipelines - on-call, incident response, SLOs, runbooks, and recovery, not just development.

• Deep, production experience with Apache Kafka (MSK preferred) - partitioning, consumer groups, idempotent semantics, Kafka Connect, and replay/backfill under load.

• Strong, hands-on production experience with Apache Flink or a comparable stateful stream processor, in Java or another JVM language - state management, checkpointing, watermarks, and exactly-once sinks.

• AWS-native data engineering across streaming, processing, storage, and catalog services (e.g. MSK, EMR, Glue, S3, Athena), with infrastructure-as-code - AWS CDK (preferred) or Terraform - CI/CD for data pipelines, and cost awareness.

• Comfort debugging distributed data pipelines (consumer lag, data skew, back pressure, checkpoint failures, late/out-of-order events) with observability tooling (e.g. Datadog/Cloud Watch).

• SQL and Python, and experience building and consuming REST APIs.

• An AI-forward engineering mindset, with demonstrated hands-on use of AI-assisted and agentic development tools, an opinion on where AI adds value…