Remote Senior Data Platform Engineer Jobs
A Senior Data Platform Engineer builds and maintains the infrastructure that enables reliable, scalable data movement and consumption across an organisation — spanning ingestion pipelines, storage systems, transformation frameworks, and the developer experience that data consumers depend on. Remote Senior Data Platform Engineers own the foundational layer that data scientists, analysts, and product teams build on, treating data infrastructure with the same engineering rigour applied to production software.
What a remote Senior Data Platform Engineer does
Day-to-day, a remote Senior Data Platform Engineer designs and implements data ingestion and transformation pipelines, manages warehouse and lake infrastructure, builds internal data tooling and SDKs, monitors data quality and pipeline reliability, and collaborates with data consumers to understand and address platform gaps. They operate as engineers first — writing production-grade code, reviewing PRs, and owning reliability outcomes — rather than analysts or consultants.
Core skills and qualifications
Strong proficiency in Python or Scala, deep experience with cloud data infrastructure (BigQuery, Snowflake, Redshift, Databricks, or Delta Lake), and hands-on ownership of orchestration systems (Airflow, Prefect, or Dagster) are the typical technical baseline. Data modeling, CDC patterns, streaming infrastructure (Kafka, Kinesis, or Pub/Sub), and dbt for transformation are increasingly standard requirements. Five to eight years of data engineering experience, with at least two spent on platform-level work, is common.
Remote work dynamics for this role
Remote Senior Data Platform Engineers must document infrastructure decisions and platform APIs clearly — data consumers in distributed teams rely on written guides and self-serve tooling to use the platform correctly. Async-first communication is well-suited to this role, given that most data platform work is deep, focused engineering rather than high-coordination delivery.
Tools and platforms
Cloud: AWS (S3, Glue, EMR, Redshift), GCP (BigQuery, Dataflow, Pub/Sub), or Azure (Synapse, Data Factory). Orchestration: Airflow, Prefect, or Dagster. Transformation: dbt. Storage: Delta Lake, Iceberg, or Hudi for lakehouse patterns. Monitoring: Monte Carlo, Great Expectations, or custom dbt test suites. Infrastructure: Terraform for cloud resource management.
Compensation benchmarks
Remote Senior Data Platform Engineers typically earn between $165,000 and $230,000 in base salary. At data-intensive companies — analytics businesses, fintech, and large-scale consumer platforms — total compensation including equity commonly exceeds $280,000. Data infrastructure expertise commands a consistent premium over generalist data engineering.
Career trajectory
Senior Data Platform Engineers typically progress toward Staff Data Engineer, Principal Data Engineer, or Head of Data Engineering. Some move toward Data Architecture or cross-functionally into Platform Engineering leadership, particularly at companies where data infrastructure merges with product platform teams.
Industry demand
Remote Senior Data Platform Engineers are in strong demand at data-driven companies across fintech, healthtech, consumer internet, and enterprise SaaS. The shift toward self-serve analytics and real-time data products has expanded headcount in data platform teams significantly over the past three years.
Frequently asked questions
How does a Data Platform Engineer differ from a Data Engineer? Data Engineers typically own specific pipelines and datasets. Data Platform Engineers own the infrastructure, tooling, and standards that all data engineers work within — the platform layer rather than the application layer.
Is streaming experience required? Increasingly, yes. Real-time data products have expanded demand for streaming expertise (Kafka, Flink, Spark Streaming). Batch-only experience limits candidacy at companies building low-latency data products, though pure-batch shops still exist.
What is the typical team structure? Data Platform teams typically sit within Data or Engineering organizations, reporting to a Head of Data Engineering or VP of Data. Team size ranges from three to fifteen engineers depending on company scale.