Data & Platform Engineer
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What does a Data & Platform Engineer do?
A Data & Platform Engineer builds and maintains the data infrastructure that enables analytics, machine learning, and AI applications to function in the first place. This role is the invisible yet critical foundation of any AI strategy: without clean data pipelines, well-designed data models, and stable platforms, any AI project remains fragile – no matter how good the models are.
Key responsibilities include designing and operating ETL and ELT pipelines, building a data lakehouse (e.g., using Databricks, Snowflake, BigQuery, or an open-source lake architecture), data modeling in tools like dbt, and managing the underlying cloud platform. Data & Platform Engineers define how data enters the organization, how it is structured, how its quality is ensured, and how different teams access it securely and efficiently.
In the AI context, the role expands: feature stores, streaming for real-time ML, data quality metrics for ML training data, and the integration of vector databases are becoming standard. Anyone who wants to scale AI seriously needs Data & Platform Engineers who can conceptualize data lake, data warehouse, streaming, and ML requirements within a coherent architecture.
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Skills and Qualifications for Data & Platform Engineers
On the technical side, we expect in-depth SQL knowledge, proficiency in Python (and ideally Scala or Java), experience with modern data stack tools such as dbt, Airflow, Dagster, or Prefect, as well as a solid understanding of at least one cloud platform (AWS, GCP, Azure), including the respective data services (Redshift, BigQuery, Snowflake, Databricks, Synapse). On the platform side, container technologies (Docker, Kubernetes), Infrastructure as Code (Terraform), CI/CD, and monitoring (Prometheus, Grafana, Datadog) are standard.
For real-time requirements, streaming technologies are also used – Kafka, Kinesis, Pub/Sub, Flink, or Spark Streaming. Those working in the areas of Data Mesh or Data Contracts also have experience with data catalogs, schema registries, and domain-driven architecture. In addition to technical skills, architectural thinking is essential. Data & Platform Engineers design structures that must last for years. They understand the trade-offs between performance, cost, governance, and developer velocity, and work closely with analytics, ML, and security teams. Strong documentation and communication skills are just as important in this cross-functional role as deep engineering expertise.
Why Data Engineering Is the Often-Underestimated Bottleneck in AI Setups
Many companies invest heavily in AI use cases without sufficiently strengthening their data foundation. The result: models that deliver inconsistent results because the data is dirty or incomplete; reports that don’t align; and ML pipelines that regularly break because upstream schemas change. Data & Platform Engineers address precisely these problems and that is exactly why their availability is crucial for serious AI initiatives.
In recruiting, the role does not make things easy for companies. Senior Data Engineers with experience in modern tech stacks, dbt, Snowflake, or Databricks, Airflow, Kubernetes, are scarce in the DACH region and highly sought after internationally. At the same time, the range of what falls under the title “Data Engineer” is vast: from ETL specialists with a classic stack to cloud-native platform engineers with a focus on streaming.
Successful hiring therefore starts with a precise job description. Anyone who posts a job listing for “Data Engineer” will receive vague applications. Those who describe in which Lakehouse, with what data volume, what latency requirements, and for which use case the role operates will attract the right candidates.

How does alphacoders find Data & Platform Engineers who truly master your tech stack?
During the briefing, we first work with you to clarify the architectural reality: Which data stack is in use, what is the platform’s maturity level, and which use cases are at the top of the roadmap? Only then do we develop the profile, persona, and scorecard and thus a search process based not on buzzwords, but on the skills actually required.
When reaching out directly, we use more than 20 channels. GitHub for engineers active in open-source tools related to dbt, Airflow, or Dagster. LinkedIn and XING for more traditional profiles. Conferences and meetups like data.engineering, dbt-Coalesce communities, or regional data tech gatherings for connected senior professionals. Our proprietary recruiting CRM connects these sources with our 770,000-strong DACH network.
During the technical interview, our tech recruiters, who have development backgrounds, specifically assess architectural understanding, trade-off thinking, and experience in real production environments. That’s exactly where it’s decided whether a person can build pipelines that will still be supporting you in five years, or deliver solutions that will need to be rewritten in twelve months.
Your benefits with alphacoders
Consulting at eye level
Large candidate pool
Multi channel search
Tested quality
Fast staffing
Experienced recruiters
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How can we support you best? Do not hesitate to contact us for a free consultation. We are looking forward to an exchange with you.
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