Sr. Data Platform Engineer
Some ghost-posting signals
- open for 160 days (90+ without a fill is a strong ghost signal)
- 667 open roles at this company in 30 days (mass-hiring blitz)
- no salary disclosed (correlates with ghost postings)
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About the role
You will be responsible for:
Key Responsibilities
End-to-End Data Migration: Architect and execute large-scale data migrations from legacy source databases to cloud targets using AWS DMS, Azure migration tools, or Airbyte.
Kubernetes Orchestration: Own and operate the Airbyte infrastructure and data pipelines running on Kubernetes, ensuring seamless production handling and rapid incident resolution.
Advanced Python Engineering: Design and build robust Python-based frameworks for automated monitoring, alerting, and high-performance ETL/ELT processing.
Platform Reliability (SRE): Ensure high availability (99.9%+) of data ingestion workflows by performing root cause analysis (RCA) and implementing self-healing infrastructure.
Cost & Performance Optimization: Manage and optimize cloud infrastructure costs (AWS/Azure) related to data sync workloads through precise resource tagging and performance tuning.
Cloud Infrastructure & Security: Configure and maintain AWS resources (S3, IAM, EKS), ensuring strict adherence to the principle of least privilege and data security standards.
Let’s talk about your skills/expertise:
Required Skills & Expertise
Experience: 5–8 years of professional experience in Data or Platform Engineering roles.
Migration Tools: Expert-level knowledge of AWS DMS, Azure Data Migration, or similar enterprise-grade replication tools.
Coding Mastery: Strong Python skills for building custom ingestion utilities, monitoring agents, and automation scripts.
Data Engineering Core: Deep understanding of Data Lakehouse architectures, dimensional modelling, and SCD Type 2 implementation.
Kubernetes Proficiency: Hands-on experience with Docker/K8s (EKS/AKS), including pod health, rollouts, and service connectivity.
Cloud Services: Solid experience with AWS (S3, IAM, CloudWatch) or Azure equivalent services.
Observability: Familiarity with logging and metrics systems (Prometheus, Grafana, or ELK) to maintain platform health.
Cultural & Operational Fit
Rapid Adaptability: Ability to quickly learn and adapt to new production environments, transitioning from design to deployment at a "production-ready" pace.
Collaborative Mindset: Act as a bridge between infrastructure and downstream data consumers to resolve complex ingestion bottlenecks.
Clean Code Advocate: Committed to Git-based workflows, CI/CD best practices, and structured documentation.
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