Data Platform Engineer

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