Senior Data Platform Engineer — Nexus Data Platform

About the team

What you'll do

- Own pipeline slices end-to-end: data ingest into a distributed streaming platform, stream-processing transform jobs, and projection of clean, contract-compliant records into downstream stores.

- Onboard new data domains and production entities — legacy schema mapping, streaming topic contracts, and stream-processing job development.

- Build and run stream-processing jobs in the cloud — transforms along the change-data-capture → streaming → processing path and batch ingestion via managed big-data services — including checkpointing, failure recovery, and job-level runbooks.

- Deploy and troubleshoot streaming and processing workloads on a container orchestration platform — job lifecycle and day-to-day application-level troubleshooting, in partnership with SRE on the underlying cluster.

- Partner with downstream teams on data contracts: schema ownership, where consumers read from, and clear boundaries between the ETL pipeline and the teams that serve on top of it.

What we’re looking for

Required

- 5+ years experience building production grade streaming pipeline systems.

- Hands-on experience with a distributed streaming/messaging platform (Kafka + Flink/Spark or similar): topic and partition design, consumer group semantics, compaction, and schema-aware serialization.

- Production experience with a distributed stream-processing engine: SQL or DataStream-style jobs, state management, checkpointing, and operational debugging of long-running jobs.

- CDC ingestion familiarity — WAL or log-based change capture into a streaming platform.

- Demonstrated experience with SQL and schema design across relational sources.

- Demonstrated experience deploying and troubleshooting streaming workloads on a container orchestration platform — basic troubleshooting of deployments and running jobs.

- Track record shipping observable,



testable pipeline components — health checks, smoke gates, benchmark-driven validation.

- Ability to work independently on assigned roadmap items while coordinating with cross-functional partners on contract boundaries.

Preferred

- Direct, hands-on experience with Apache Kafka and Apache Flink

- Familiarity with Debezium CDC capture.

- Experience projecting streaming data into an OLAP or analytical serving engine (StarRocks, ClickHouse, Druid, Pinot, or similar).

- Awareness of Gov Cloud deployment constraints and data residency considerations.

- Experience with multi-tenant SaaS data workloads (tenant partitioning, hot-partition skew).

How we work

- AI-assisted development —

we use coding agents and automation to move fast; you should be comfortable reviewing and directing AI-generated changes, not writing every line by hand.

- Local-first iteration —

every engineer has a local stack to develop and test pipeline changes before promoting to shared environments; schema and job work should be fast to try without waiting on central infrastructure.

- Prove it before you ship —

automated smoke tests and performance benchmarks are part of how we validate changes, not a handoff to someone else at the end.

- Cross-team by design —

data contracts between the pipeline and downstream teams are explicit; we coordinate early with external teams so downstream consumers stay aligned without slowing delivery.

What success looks like in the first 6 months

- At least one production data domain onboarded through Kafka and Flink end-to-end, with automated validation passing in local and shared environments.

- A partner team consuming pipeline output under a documented contract — clear access patterns and agreed SLA baselines.

- Deploy and integration workflow dependable enough for routine use, not a manual heroics exercise each time.

- Contributed runbooks, automation, or reusable patterns that make onboarding the next domain meaningfully faster.

📌 Data Engineer with Apache Flink (México)
🏢 E-IT
📍 México