Our Freelance Platform Engineers design, implement, and optimize scalable data platforms—from lakehouse architecture and data pipeline development to governance frameworks and monitoring setups. They deliver concrete deliverables: operational ingestion pipelines, documented data models, configured orchestration environments, and production-ready cloud infrastructures based on AWS, Azure, or GCP. For companies that want to make data-driven decisions, a stable, high-performance platform isn’t just an option—it’s a prerequisite.
Typical triggers for engaging our experts include building a new data platform from the ground up, migrating legacy systems to modern cloud environments, or scaling existing pipelines to handle growing data volumes. Even if internal capacity for a critical data project is lacking or an existing setup requires a fundamental overhaul, the time to act is now—before technical debt blocks further development.
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A Data Platform Engineer builds and maintains the technical infrastructure needed to ensure that data products can be developed securely, scalably, and efficiently.
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Our experts focus on operability, standards, and reusability. They integrate architecture, infrastructure, and data workloads into a stable, cohesive system. This creates platform foundations upon which teams can deliver reliably.
With these profiles, you establish policies, access concepts, and data quality correctly from the start—rather than adding them as an afterthought. This reduces audit risks and rework. At the same time, deployment and operations remain practical for teams.
Our profiles optimize compute, storage, and orchestration based on real workload profiles. They provide transparency regarding usage, SLAs, and cost levers. This allows you to scale data products without causing your cloud bill to skyrocket.
Assignments for Freelance Data Platform Engineer usually come up in projects around Data Analytics Consulting. That page explains what the field covers, when external support makes sense and which roles belong to it. Adjacent field: AI Consulting.
We work with you to define the technical scope of your data project: platform architecture, cloud environment used, data volume, team composition, and specific success criteria. This ensures that we’re not just looking for technology keywords, but for the profile that will truly drive your specific setup forward.
Based on your requirements, we match your project with our verified profiles—taking into account proven platform experience, cloud ecosystem, and project complexity. We’ll introduce you to suitable candidates within 24–36 hours.
What matters to us isn’t the number of technologies listed in the profile, but whether your data platform ultimately runs reliably, scales, and can be maintained internally. Our experts deliver documented, transferable results—not black-box engineering.
Data Platform Engineer specializing in Lakehouse platforms and self-service enablement. Areas of expertise: Databricks on Azure, Terraform/IaC, Unity Catalog, CI/CD for data products, observability (OpenTelemetry), cost optimization, and golden paths for data teams.
Data Platform Engineer specializing in scalable data pipelines and operational reliability. Areas of expertise: AWS (S3, IAM, Glue), Airflow, dbt, Spark, data quality gates, RBAC/ABAC, monitoring/alerting, and incident and runbook design for production data workloads.
Data Platform Engineer specializing in governance, security, and data cataloging. Areas of expertise: Snowflake, BigQuery, data catalog/lineage, masking/tokenization, policy-as-code, data contracts, access reviews, audit trails, and secure approval processes for PII.
Data Platform Engineer specializing in streaming and real-time architectures. Areas of expertise: Kafka, Schema Registry, CDC (Debezium), Flink/Spark Structured Streaming, event-driven data products, backfill strategies, SLOs/SLIs, and secure multi-tenant platforms.
We’ll send you a curated selection of suitable profiles within 24–36 hours. The profiles are tailored to your tech stack, security requirements, and preferred operating model. We’ll then coordinate interviews at short notice and assist with the technical pre-screening.
A Data Platform Engineer develops and operates the data platform on which data engineers, analytics, and ML teams work. They automate provisioning and deployments, implement observability, security, and governance, and ensure reliable pipelines. The goal is a scalable, cost-effective, and compliant platform with clear standards and self-service capabilities.
If data products are regularly unstable, releases become risky, or teams have to wait for tickets regarding access and environments, the need is high. Rapidly rising cloud costs, a lack of lineage, or recurring audit findings are also typical signs. With these profiles, you can quickly establish structure in platform standards, operations, and governance.
Key requirements include cloud and platform expertise (AWS/Azure/GCP), IaC (Terraform), CI/CD, containers/Kubernetes, and a strong focus on security and IAM. In terms of tools, Airflow, dbt, Spark, Kafka, Snowflake/BigQuery/Databricks, and observability stacks for logs, metrics, and traces are often relevant. Useful certifications include, for example, AWS/Azure/GCP Professional-level certifications, Databricks certifications, or security/compliance credentials—but what really matters is demonstrable experience implementing these technologies in production environments.
A Data Engineer primarily delivers data pipelines and data models for specific use cases and functional areas. A Data Platform Engineer builds the underlying platform layer: standards, deployments, access policies, observability, operational models, and reusable components. With these profiles, you can improve the delivery capabilities of multiple teams simultaneously, rather than optimizing individual pipelines.
Typical deliverables include a platform architecture—comprising a target state, operational model, and SLAs—as well as IaC and CI/CD components for reproducible environments. In addition, there are security and governance artifacts such as RBAC/ABAC models, policy-as-code, catalog/lineage setup, and audit trails. They also frequently provide observability dashboards, runbooks, cost levers (FinOps), and templates/golden paths for self-service.
The daily rate for our profiles typically ranges from €900 to €1,200. The specific rate depends, among other factors, on the cloud stack, security requirements, seniority, and responsibility for operations/on-call duty. For a reliable assessment, the profiles provide transparent details on key skills and project focus areas.
