Piper Companies is seeking a Data / Platform Engineer to join a growing technology organization on a long-term contract with strong potential for conversion. This fully remote, US-based role focuses on building and operating the core data platform that powers large-scale integrations, analytics, and event-driven services.
Responsibilities:
- Own and evolve multi-stage data pipelines that ingest data from external sources including APIs, direct queries, and file-based feeds into a centralized lake and warehouse
- Build and maintain event-driven services and ETL workflows that enrich, deduplicate, and validate data at scale
- Design and enforce data contracts, schemas, and cutover strategies to support safe backfills, corrections, and parity with legacy systems
- Implement observability across pipelines and jobs, including monitoring, alerting, lineage, and operational runbooks
- Operate and secure cloud-based data infrastructure on AWS using infrastructure-as-code practices
- Optimize data models and SQL performance for large-scale analytical workloads
- Partner with engineering and product teams to support downstream integrations, APIs, and user-facing data access
- Document architecture, standards, and workflows to support a growing and collaborative platform team
Requirements:
- 5+ years of experience in backend and/or data engineering roles with ownership of production data platforms
- Strong hands-on experience building event-driven or batch data pipelines using Python, Node, or similar languages
- Deep experience operating AWS-based systems and provisioning infrastructure using Terraform
- Advanced SQL skills with experience tuning performance in MPP data stores such as Redshift, Snowflake, or equivalent
- Proven experience delivering reliable, idempotent data pipelines and validating outputs against legacy systems
- Experience working in regulated or highly governed environments (e.g., healthcare, financial services)
- Strong understanding of security, compliance, and audit-readiness best practices (e.g., SOC 2, ISO 27001)
- Comfortable owning systems end-to-end and troubleshooting complex production issues
- Workflow orchestration tools such as Airflow or similar
- Streaming and messaging technologies (Kafka, Kinesis, SQS)
- Data modeling and transformation tools (e.g., dbt)
- Data quality, metadata, and lineage tooling
- Exposure to real-time enrichment or rules-based processing systems