We are looking for a hybrid Data & DevOps Engineer to manage our end-to-end data lifecycle. You will not only maintain the cloud infrastructure but also build and optimize the ingestion engines that feed our Databricks Lakehouse. A key part of this role involves interacting with REST APIs, managing serverless ingestion (AWS Lambda), and ensuring data quality from the moment it hits S3.

Key responsibilities

1. IaC & Security Hardening: Expand our AWS infrastructure using Terraform or CloudFormation. You will implement rigorous security measures, including VPC peering/PrivateLink for Databricks, KMS encryption at rest, and IAM least-privilege policies.

2. API Ingestion & Engineering: Build and maintain Python-based ingestion services. You will manage API authentication, handle rate limiting, and ensure efficient data partitioning in S3.

3. CI/CD Evolution: Scale our GitHub Actions workflows to handle multi environment deployments (Dev/Sandbox/Prod) for both cloud infrastructure (Glue and Kinesis) and Databricks DLT pipelines.4. Spark Performance & Optimization: Monitor and tune Spark configurations (shuffling, partitioning, caching) to ensure our DLT and AutoLoader pipelines run efficiently.

5. MLOps Support: Partner with Data Scientists to automate ML model deployments, managing feature store integrations and model serving infrastructure.

6. Security & Governance: Implement SSE-KMS encryption, IAM policies, and lifecycle rules to ensure our data lake is compliant and cost-effective.

7. Observability & Monitoring: Build a "single pane of glass" using CloudWatch and Datadog. You’ll create dashboards that track pipeline latency, AutoLoader costs, and system health.

8. Documentation & Knowledge Transfer: Produce high-quality architectural diagrams and runbooks. You aren't just building; you are mentoring the internal team to ensure long-term operational success.

Requirements

Must-have:

• AWS Serverless: Hands-on with Lambda, EventBridge, SQS, SNS, and S3.

• Databricks: Experience with Delta Live Tables (DLT), AutoLoader, and Unity Catalog.

• DevOps: Proficient in GitHub Actions and Terraform.

• Monitoring: Hands-on experience with Datadog and CloudWatch Logs/Metrics.

• Languages: Strong Python (for Lambda/PySpark) and SQL (for data validation/modelling).

• Data Prep: Knowledge of AWS Glue (Catalog/ETL) and Kinesis Firehose is a major plus.

Compensation & structure

• Competitive salary appropriate for an early-stage European startup.

• Company equity

⚡Join Tether for…

• A central role in defining how EV charging flexibility is sold in Europe

• Direct collaboration with the management team on product, data, and cloud infrastructure

• High autonomy, fast decision cycles, and meaningful equity/ownership discussions for the right profile