Data Platform Engineer (Python / Golang)

What project we have for you

The platform is a multi-tenant SaaS solution for enterprise fleet management — built on a microservices architecture running on AWS across multiple accounts and environments, processing continuous telemetry streams from connected vehicles, and serving web, mobile, and third-party consumers through a set of REST and event-driven APIs. The backend is Python/Django and Golang, the frontend is React, mobile is native iOS (SwiftUI) and Android (Kotlin), and the infrastructure runs on EKS with Terraform-managed IaC, Jenkins/GitHub Actions CI/CD, and a full observability stack (Prometheus, Grafana, Elastic APM, PagerDuty on-call).

What you will d

• oOwn data platform architecture across platform — streaming pipelines, partner integrations, and core backend service
• sDefine and enforce backend and data engineering standards: service contracts, error handling, logging, secrets managemen
• tOwn code quality and architectural consistenc
• yOwn event-driven integrations with 20+ external data partners — data contracts, ingestion, transformation, failure handlin
• gDesign and govern data models across PostgreSQL, DynamoDB, S3, and analytical system
• sDefine and maintain IaC architecture for owned services using Terrafor
• mCollaborate with DevOps on deployment patterns, observability, and incident runbook
• sMonitor production systems, drive alerting standards, and lead resolution of critical data incident
• sRepresent backend and data constraints in planning and API contract discussions — raises risks before implementation start
• sProduce and maintain architecture documentation, ADRs, and onboarding material
• sParticipate in planning, estimation, and technical risk discussions at program leve
• lUse AI-assisted development tools responsibly — review output critically, ensure quality and security complianc

e
What you need for th

• is5+ years Python backend in production; 2+ years as a lead or principal on a data-heavy platfo
• rmTyped Python at depth: Pydantic, mypy/pyright, clean code and OOP principles; sets standards the team follo
• wsAWS data services in production (must-have): Lambda, Kinesis, DynamoDB, S3, Athena/Presto or Redshift, SQS/SNS — owned and operated end-to-end, not just us
• edEvent-driven architecture ownership — designs streaming pipelines, DLQ strategies, retry/idempotency patterns; makes the calls others foll
• owData engineering at principal depth: ETL/ELT, schema validation, data contracts, data lake/warehouse architecture; defines patterns the team reus
• esSQL at depth — PostgreSQL and analytical engines (Athena, Redshift); query optimisation, partitioning, large-scale zero-downtime migratio
• nsGo (Golang) — 2+ years in production; defines Go service patterns, reviews and approves Go code across the te
• amTerraform — owns IaC architecture for owned services; defines module structure, not just extends existing on
• esTesting discipline — pytest, pytest-mock, moto; drives testing culture and standards across the te
• amApache Kafka at scale — topic and partition design, consumer group management, schema registry, dead-letter handli
• ngProduces ADRs, integration design docs, data model documentatio

n;
Nice to ha

• ve:AWS Powertools, Glue, Step Functions, EventBri
• dgeOpenTelemetry, Grafana, Prometheus / Elastic
• APMdbt or Airf
• lowTelematics, IoT, or fleet domain backgro