Data Platform Engineer (Python / Golang) Job at Intellias

Job Description

You will work on a live, high-load fleet management platform that connects tens of thousands of vehicles across enterprise fleets worldwide — processing real-time telemetry, powering mobile apps used by drivers and technicians on the ground, and integrating with hardware, firmware, and 20+ data partners. The system runs 24/7, handles genuine scale, and the work is a mix of complex new features, infrastructure modernisation, and keeping production rock-solid. If you want a project where the data is real, the stakes are real, and the engineering problems are interesting — this is it.

Job Responsibility

Own data platform architecture across platform — streaming pipelines, partner integrations, and core backend services
Define and enforce backend and data engineering standards: service contracts, error handling, logging, secrets management
Own code quality and architectural consistency
Own event-driven integrations with 20+ external data partners — data contracts, ingestion, transformation, failure handling
Design and govern data models across PostgreSQL, DynamoDB, S3, and analytical systems
Define and maintain IaC architecture for owned services using Terraform
Collaborate with DevOps on deployment patterns, observability, and incident runbooks
Monitor production systems, drive alerting standards, and lead resolution of critical data incidents
Represent backend and data constraints in planning and API contract discussions — raises risks before implementation starts
Produce and maintain architecture documentation, ADRs, and onboarding materials
Participate in planning, estimation, and technical risk discussions at program level
Use AI-assisted development tools responsibly — review output critically, ensure quality and security compliance

Requirements

5+ years Python backend in production
2+ years as a lead or principal on a data-heavy platform
Typed Python at depth: Pydantic, mypy/pyright, clean code and OOP principles
sets standards the team follows
AWS data services in production (must-have): Lambda, Kinesis, DynamoDB, S3, Athena/Presto or Redshift, SQS/SNS — owned and operated end-to-end, not just used
Event-driven architecture ownership — designs streaming pipelines, DLQ strategies, retry/idempotency patterns
makes the calls others follow
Data engineering at principal depth: ETL/ELT, schema validation, data contracts, data lake/warehouse architecture
defines patterns the team reuses
SQL at depth — PostgreSQL and analytical engines (Athena, Redshift)
query optimisation, partitioning, large-scale zero-downtime migrations
Go (Golang) — 2+ years in production
defines Go service patterns, reviews and approves Go code across the team
Terraform — owns IaC architecture for owned services
defines module structure, not just extends existing ones
Testing discipline — pytest, pytest-mock, moto
drives testing culture and standards across the team
Apache Kafka at scale — topic and partition design, consumer group management, schema registry, dead-letter handling
Produces ADRs, integration design docs, data model documentation

Nice to have

AWS Powertools, Glue, Step Functions, EventBridge
OpenTelemetry, Grafana, Prometheus / Elastic APM
dbt or Airflow
Telematics, IoT, or fleet domain background

What we offer

Comfortable atmosphere
support for well-being
charge professional growth
equity, diversity, and inclusion

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Data Platform Engineer (Python / Golang)

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