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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Platform Engineer - **Company:** Tatari, Inc. - **Location:** Los Angeles, CA, United States - **Experience:** Expert - **Salary:** $190,000.0 - $240,000.0 - **Contract:** Permanent contract - **Skills:** Airflow, Amazon Web Services, Microsoft Azure, Bash Shell, Big Data, Cloud Computing, Databases, Information Engineering, Data Infrastructure, Extract Transform Load (ETL), Data Systems, Linux, Distributed Computing Environment, Domain Name System (DNS), Identity and Access Management, Job Scheduling, Python (Programming Language), Key Management, PostgreSQL, Online Analytical Processing, Network Architecture, Online Transaction Processing, Queueing Systems, Prometheus, Datadog, Data Logging, Scripting, Google Cloud, Load Balancing, Data Ingestion, Autoscaling, System Availability, Apache Spark, Indexer, Containerization, Pyspark, Kubernetes, Apache Flink, Apache Kafka, Data Management, Database Replication, Machine Learning Operations, Vertica, Kibana, Terraform, Stream Processing, Docker, Amazon Redshift, Databricks - **Published:** July 28, 2026 - **Apply:** https://www.dice.com/job-detail/c93d631c-4da0-4c03-af1a-32e1c949e4a1 ## About the Role * 3+ years in cloud infrastructure, SRE, or platform engineering (AWS preferred; Google Cloud Platform/Azure experience translates) * High Availability architecture: blue/green deployments, data replication, load balancing * Experience with workflow orchestration (Airflow or similar DAG-based schedulers - or general job scheduling/cron systems at scale) * Strong Linux fundamentals and scripting (Bash, Python, or similar) * Distributed data processing (Spark, PySpark, or similar big data frameworks - or experience managing clusters that run them) * Containerization and orchestration (Kubernetes, Docker, or similar) * Data ingestion, ETL, or streaming systems (Kafka, Flink, or similar - or experience operating message queues and pipelines) * Infrastructure-as-code and provisioning (Terraform, Helm, or similar) * OLAP and OLTP databases (Clickhouse, Postgres, Redshift, or similar - query patterns, indexing, and operational care) * Monitoring, logging, and observability (Datadog, Prometheus, Kibana, or similar) * Managed data platforms (Databricks or similar - administering and scaling, not just consuming) * Network infrastructure fundamentals: load balancers, DNS, auto-scaling, multi-region topologies, proxies * Security and access management: least-privilege, secrets management, controls for data systems * MLOps concepts or tooling - a plus What we value above technical skillsWe are explicitly willing to trade depth in data tooling for the right operational character. Specifically: * Humility - you don't know everything, you say so, and you ask before acting in unfamiliar territory * Methodical execution - you minimize variables, you don't premature-optimize, you finish what you started before starting something new * Communication - you tell the team what you're doing before you do it, especially in shared or production environments ## Description This is a systems and infrastructure position first. As a Data Platform Engineer, you will be responsible for the reliability, stability, and operational health of our data platform - including how it is deployed, monitored, maintained, and promoted across environments. Data engineering skills are a plus and will be developed on the job; what we cannot teach is operational discipline. If you have spent your career keeping production systems alive, know what it feels like to break prod and never want to do it again, and treat lower environments as non-negotiable gates rather than suggestions - we want to talk to you. This is not a data engineering role. You will not spend most of your time writing jobs or consuming the platform. You will be administering, scaling, hardening, and evolving it.Responsibilities * Own the reliability and availability of our data platform infrastructure across all environments * Enforce and improve environment promotion discipline - staging is not prod, and prod is sacred * Define and uphold SOPs around deployments, maintenance windows, and change management * Instrument and monitor platform health using observability tooling; build alerting that means something * Participate in architecture and deployment discussions; push back when something isn't ready * Collaborate with data scientists, engineers, and product managers on infrastructure needs - as a partner, not an order-taker * Identify and remediate reliability risks before they become incidents * Support customer-facing and internal systems with a bias toward stability over velocity, * Operational instinct - "the fear" - you've been burned by prod, you respect it, and you've built habits around it. You know what a proper maintenance window looks like, you communicate before you touch production, and you don't spin up new initiatives while something critical is still burning in. ## Related Videos - [Docker network without Docker](https://www.wearedevelopers.com/videos/1418-docker-network-without-docker) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Debug a Kubernetes Operator](https://www.wearedevelopers.com/videos/487-debug-a-kubernetes-operator) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [Docker exec without Docker](https://www.wearedevelopers.com/videos/1094-docker-exec-without-docker) - [Docker build without Docker](https://www.wearedevelopers.com/videos/100114-docker-build-without-docker) ## Related Articles - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [Top Big Data Technologies That You Need to Know](https://www.wearedevelopers.com/magazine/108-top-big-data-technologies-that-you-need-to-know) - [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [Dev Digest 120 - Apple and peers](https://www.wearedevelopers.com/magazine/455-dev-digest-120-apple-and-peers)