{"id":677,"date":"2026-01-22T13:01:26","date_gmt":"2026-01-22T13:01:26","guid":{"rendered":"https:\/\/datascientists.info\/?p=677"},"modified":"2026-01-22T13:01:26","modified_gmt":"2026-01-22T13:01:26","slug":"the-data-engineer-role-in-a-ml-pipeline","status":"publish","type":"post","link":"https:\/\/datascientists.info\/index.php\/2026\/01\/22\/the-data-engineer-role-in-a-ml-pipeline\/","title":{"rendered":"The Data Engineer Role in a ML Pipeline"},"content":{"rendered":"\n
Data engineers provide the critical foundation for every successful Machine Learning (ML) deployment, supporting the powerful models and insights that often grab headlines. While data scientists focus on model development and evaluation, data engineers ensure that the right data is collected, processed, and made available in a reliable and scalable way.<\/p>\n\n\n\n
1. The Overlooked Hero<\/strong> <\/h3>\n\n\n\n
Data engineers rarely get the spotlight, but their role is indispensable in any ML project. A ML pipeline is only as good as the data it runs on, and without a solid data infrastructure, even the most sophisticated models can fail.<\/p>\n\n\n\n
2. What Is an ML Pipeline?<\/strong><\/h3>\n\n\n\n
A ML pipeline is a series of automated, repeatable steps that allow data to flow from raw input to model training, evaluation, and deployment. Key stages include:<\/p>\n\n\n\n
\n
Data ingestion <\/li>\n\n\n\n
Data validation and cleaning <\/li>\n\n\n\n
Feature engineering <\/li>\n\n\n\n
Model training and tuning <\/li>\n\n\n\n
Model deployment and monitoring <\/li>\n<\/ul>\n\n\n\n
While data scientists might be more involved in the later stages, data engineers are primarily responsible for the early and middle parts\u2014building and maintaining the infrastructure that powers the whole process.<\/p>\n\n\n\n<\/figure>\n\n\n\n
3. Responsibilities of a Data Engineer in the ML Pipeline<\/strong><\/h3>\n\n\n\n
a. Data Ingestion and Integration<\/strong><\/h4>\n\n\n\n
Data engineers are responsible for collecting data from various sources\u2014databases, APIs, event logs, IoT devices, and third-party services. They ensure real-time or batch pipelines are reliable and scalable.<\/p>\n\n\n\n
b. Data Cleaning and Validation<\/strong><\/h4>\n\n\n\n
Poor quality data can cripple an ML model. Data engineers create pipelines that clean, deduplicate, and validate incoming data to ensure consistency and accuracy.<\/p>\n\n\n\n
c. Feature Store Management<\/strong><\/h4>\n\n\n\n
Data engineers help build and manage feature stores, which are centralized repositories of curated features that can be reused across models. This ensures consistency and avoids duplication of effort.<\/p>\n\n\n\n
d. Workflow Orchestration<\/strong><\/h4>\n\n\n\n
They use tools like Apache Airflow, Kubeflow, or Prefect to orchestrate complex workflows ensuring tasks like data transformation, training jobs, and evaluation processes run in sequence and on schedule.<\/p>\n\n\n\n
e. Monitoring and Logging<\/strong><\/h4>\n\n\n\n
Once models are deployed, data engineers help monitor data drift, ensure data freshness, and set up alerting mechanisms for broken pipelines or anomalies.<\/p>\n\n\n\n
4. Collaboration with Data Scientists and ML Engineers<\/strong><\/h3>\n\n\n\n
Data engineers work closely with:<\/p>\n\n\n\n
\n
Data Scientists<\/strong> to ensure access to clean, well-structured data. <\/li>\n\n\n\n
ML Engineers<\/strong> to integrate pipelines into production environments. <\/li>\n\n\n\n
DevOps\/Platform Engineers<\/strong> to maintain infrastructure and CI\/CD workflows. <\/li>\n<\/ul>\n\n\n\n
5. Essential Tools and Technologies<\/strong><\/h3>\n\n\n\n
Some common tools in the data engineer\u2019s toolkit include:<\/p>\n\n\n\n
Storage<\/strong>: S3, HDFS, Delta Lake <\/li>\n<\/ul>\n\n\n\n
6. Why This Role Matters More Than Ever<\/strong><\/h3>\n\n\n\n
As businesses adopt more complex ML systems, the demand for production-grade data infrastructure is growing. Data engineers are central to making ML scalable, maintainable, and trustworthy.<\/p>\n\n\n\n
7. Conclusion<\/strong><\/h3>\n\n\n\n
In the same way that skyscrapers need architects and solid foundations, ML pipelines need data engineers. Their work may be behind the scenes, but it’s what keeps models alive and accurate in production. Investing in strong data engineering isn\u2019t optional it\u2019s essential.<\/p>\n","protected":false},"excerpt":{"rendered":"
Data engineers provide the critical foundation for every successful Machine Learning (ML) deployment, supporting the powerful models and insights that often grab headlines. While data scientists focus on model development and evaluation, data engineers ensure that the right data is collected, processed, and made available in a reliable and scalable way. 1. The Overlooked Hero […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[125],"tags":[94,146,29,52],"ppma_author":[144,145],"class_list":["post-677","post","type-post","status-publish","format-standard","hentry","category-data-engineering","tag-data-engineer","tag-data-pipeline","tag-data-science","tag-machine-learning","author-marc","author-saidah"],"yoast_head":"\n
The Data Engineer Role in a ML Pipeline - DATA DO - \u30c7\u30fc\u30bf \u9053<\/title>\n<meta name=\"description\" content=\"The role of the data engineer is to ensure that the right data is collected, processed, and made available in a reliable and scalable way.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/datascientists.info\/index.php\/2026\/01\/22\/the-data-engineer-role-in-a-ml-pipeline\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"The Data Engineer Role in a ML Pipeline - DATA DO - \u30c7\u30fc\u30bf \u9053\" \/>\n<meta property=\"og:description\" content=\"The role of the data engineer is to ensure that the right data is collected, processed, and made available in a reliable and scalable way.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/datascientists.info\/index.php\/2026\/01\/22\/the-data-engineer-role-in-a-ml-pipeline\/\" \/>\n<meta property=\"og:site_name\" content=\"DATA DO - \u30c7\u30fc\u30bf \u9053\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/DataScientists\/\" \/>\n<meta property=\"article:published_time\" content=\"2026-01-22T13:01:26+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/datascientists.info\/wp-content\/uploads\/2026\/01\/data_engineer_image.png\" \/>\n<meta name=\"author\" content=\"Marc Matt, Saidah Kafka\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Marc Matt\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"3 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\\\/\\\/datascientists.info\\\/index.php\\\/2026\\\/01\\\/22\\\/the-data-engineer-role-in-a-ml-pipeline\\\/#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/datascientists.info\\\/index.php\\\/2026\\\/01\\\/22\\\/the-data-engineer-role-in-a-ml-pipeline\\\/\"},\"author\":{\"name\":\"Marc Matt\",\"@id\":\"https:\\\/\\\/datascientists.info\\\/#\\\/schema\\\/person\\\/723078870bf3135121086d46ebb12f19\"},\"headline\":\"The Data Engineer Role in a ML Pipeline\",\"datePublished\":\"2026-01-22T13:01:26+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\\\/\\\/datascientists.info\\\/index.php\\\/2026\\\/01\\\/22\\\/the-data-engineer-role-in-a-ml-pipeline\\\/\"},\"wordCount\":505,\"publisher\":{\"@id\":\"https:\\\/\\\/datascientists.info\\\/#organization\"},\"image\":{\"@id\":\"https:\\\/\\\/datascientists.info\\\/index.php\\\/2026\\\/01\\\/22\\\/the-data-engineer-role-in-a-ml-pipeline\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/datascientists.info\\\/wp-content\\\/uploads\\\/2026\\\/01\\\/data_engineer_image.png\",\"keywords\":[\"Data Engineer\",\"Data Pipeline\",\"Data Science\",\"Machine Learning\"],\"articleSection\":[\"Data Engineering\"],\"inLanguage\":\"en-US\"},{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/datascientists.info\\\/index.php\\\/2026\\\/01\\\/22\\\/the-data-engineer-role-in-a-ml-pipeline\\\/\",\"url\":\"https:\\\/\\\/datascientists.info\\\/index.php\\\/2026\\\/01\\\/22\\\/the-data-engineer-role-in-a-ml-pipeline\\\/\",\"name\":\"The Data Engineer Role in a ML Pipeline - DATA DO - \u30c7\u30fc\u30bf \u9053\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/datascientists.info\\\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\\\/\\\/datascientists.info\\\/index.php\\\/2026\\\/01\\\/22\\\/the-data-engineer-role-in-a-ml-pipeline\\\/#primaryimage\"},\"image\":{\"@id\":\"https:\\\/\\\/datascientists.info\\\/index.php\\\/2026\\\/01\\\/22\\\/the-data-engineer-role-in-a-ml-pipeline\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/datascientists.info\\\/wp-content\\\/uploads\\\/2026\\\/01\\\/data_engineer_image.png\",\"datePublished\":\"2026-01-22T13:01:26+00:00\",\"description\":\"The role of the data engineer is to ensure that the right data is collected, processed, and made available in a reliable and scalable way.\",\"breadcrumb\":{\"@id\":\"https:\\\/\\\/datascientists.info\\\/index.php\\\/2026\\\/01\\\/22\\\/the-data-engineer-role-in-a-ml-pipeline\\\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\\\/\\\/datascientists.info\\\/index.php\\\/2026\\\/01\\\/22\\\/the-data-engineer-role-in-a-ml-pipeline\\\/\"]}]},{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/datascientists.info\\\/index.php\\\/2026\\\/01\\\/22\\\/the-data-engineer-role-in-a-ml-pipeline\\\/#primaryimage\",\"url\":\"https:\\\/\\\/datascientists.info\\\/wp-content\\\/uploads\\\/2026\\\/01\\\/data_engineer_image.png\",\"contentUrl\":\"https:\\\/\\\/datascientists.info\\\/wp-content\\\/uploads\\\/2026\\\/01\\\/data_engineer_image.png\",\"width\":1024,\"height\":1024},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\\\/\\\/datascientists.info\\\/index.php\\\/2026\\\/01\\\/22\\\/the-data-engineer-role-in-a-ml-pipeline\\\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\\\/\\\/datascientists.info\\\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"The Data Engineer Role in a ML Pipeline\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\\\/\\\/datascientists.info\\\/#website\",\"url\":\"https:\\\/\\\/datascientists.info\\\/\",\"name\":\"Data Scientists\",\"description\":\"Digging data, Big Data, Analysis, Data Mining\",\"publisher\":{\"@id\":\"https:\\\/\\\/datascientists.info\\\/#organization\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\\\/\\\/datascientists.info\\\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"en-US\"},{\"@type\":\"Organization\",\"@id\":\"https:\\\/\\\/datascientists.info\\\/#organization\",\"name\":\"DATA DO - \u30c7\u30fc\u30bf \u9053\",\"url\":\"https:\\\/\\\/datascientists.info\\\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/datascientists.info\\\/#\\\/schema\\\/logo\\\/image\\\/\",\"url\":\"https:\\\/\\\/datascientists.info\\\/wp-content\\\/uploads\\\/2026\\\/02\\\/Bildschirmfoto-vom-2026-02-02-08-13-21.png\",\"contentUrl\":\"https:\\\/\\\/datascientists.info\\\/wp-content\\\/uploads\\\/2026\\\/02\\\/Bildschirmfoto-vom-2026-02-02-08-13-21.png\",\"width\":250,\"height\":174,\"caption\":\"DATA DO - \u30c7\u30fc\u30bf \u9053\"},\"image\":{\"@id\":\"https:\\\/\\\/datascientists.info\\\/#\\\/schema\\\/logo\\\/image\\\/\"},\"sameAs\":[\"https:\\\/\\\/www.facebook.com\\\/DataScientists\\\/\"]},{\"@type\":\"Person\",\"@id\":\"https:\\\/\\\/datascientists.info\\\/#\\\/schema\\\/person\\\/723078870bf3135121086d46ebb12f19\",\"name\":\"Marc Matt\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/secure.gravatar.com\\\/avatar\\\/74f48ef754cf04f628f42ed117a3f2b42931feeb41a3cca2313b9714a7d4fdd2?s=96&d=mm&r=g53b84b5f47a2156ba8b047d71d6d05fc\",\"url\":\"https:\\\/\\\/secure.gravatar.com\\\/avatar\\\/74f48ef754cf04f628f42ed117a3f2b42931feeb41a3cca2313b9714a7d4fdd2?s=96&d=mm&r=g\",\"contentUrl\":\"https:\\\/\\\/secure.gravatar.com\\\/avatar\\\/74f48ef754cf04f628f42ed117a3f2b42931feeb41a3cca2313b9714a7d4fdd2?s=96&d=mm&r=g\",\"caption\":\"Marc Matt\"},\"description\":\"Senior Data Architect with 15+ years of experience helping Hamburg's leading enterprises modernize their data infrastructure. I bridge the gap between legacy systems (SAP, Hadoop) and modern AI capabilities. I help clients: Migrate & Modernize: Transitioning on-premise data warehouses to Google Cloud\\\/AWS to reduce costs and increase agility. Implement GenAI: Building secure RAG (Retrieval-Augmented Generation) pipelines to unlock value from internal knowledge bases using LangChain and Vector DBs. Scale MLOps: Operationalizing machine learning models from PoC to production with Kubernetes and Airflow. Proven track record leading engineering teams.\",\"sameAs\":[\"https:\\\/\\\/data-do.de\"]}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"The Data Engineer Role in a ML Pipeline - DATA DO - \u30c7\u30fc\u30bf \u9053","description":"The role of the data engineer is to ensure that the right data is collected, processed, and made available in a reliable and scalable way.","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/datascientists.info\/index.php\/2026\/01\/22\/the-data-engineer-role-in-a-ml-pipeline\/","og_locale":"en_US","og_type":"article","og_title":"The Data Engineer Role in a ML Pipeline - DATA DO - \u30c7\u30fc\u30bf \u9053","og_description":"The role of the data engineer is to ensure that the right data is collected, processed, and made available in a reliable and scalable way.","og_url":"https:\/\/datascientists.info\/index.php\/2026\/01\/22\/the-data-engineer-role-in-a-ml-pipeline\/","og_site_name":"DATA DO - \u30c7\u30fc\u30bf \u9053","article_publisher":"https:\/\/www.facebook.com\/DataScientists\/","article_published_time":"2026-01-22T13:01:26+00:00","og_image":[{"url":"https:\/\/datascientists.info\/wp-content\/uploads\/2026\/01\/data_engineer_image.png","type":"","width":"","height":""}],"author":"Marc Matt, Saidah Kafka","twitter_card":"summary_large_image","twitter_misc":{"Written by":"Marc Matt","Est. reading time":"3 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/datascientists.info\/index.php\/2026\/01\/22\/the-data-engineer-role-in-a-ml-pipeline\/#article","isPartOf":{"@id":"https:\/\/datascientists.info\/index.php\/2026\/01\/22\/the-data-engineer-role-in-a-ml-pipeline\/"},"author":{"name":"Marc Matt","@id":"https:\/\/datascientists.info\/#\/schema\/person\/723078870bf3135121086d46ebb12f19"},"headline":"The Data Engineer Role in a ML Pipeline","datePublished":"2026-01-22T13:01:26+00:00","mainEntityOfPage":{"@id":"https:\/\/datascientists.info\/index.php\/2026\/01\/22\/the-data-engineer-role-in-a-ml-pipeline\/"},"wordCount":505,"publisher":{"@id":"https:\/\/datascientists.info\/#organization"},"image":{"@id":"https:\/\/datascientists.info\/index.php\/2026\/01\/22\/the-data-engineer-role-in-a-ml-pipeline\/#primaryimage"},"thumbnailUrl":"https:\/\/datascientists.info\/wp-content\/uploads\/2026\/01\/data_engineer_image.png","keywords":["Data Engineer","Data Pipeline","Data Science","Machine Learning"],"articleSection":["Data Engineering"],"inLanguage":"en-US"},{"@type":"WebPage","@id":"https:\/\/datascientists.info\/index.php\/2026\/01\/22\/the-data-engineer-role-in-a-ml-pipeline\/","url":"https:\/\/datascientists.info\/index.php\/2026\/01\/22\/the-data-engineer-role-in-a-ml-pipeline\/","name":"The Data Engineer Role in a ML Pipeline - DATA DO - \u30c7\u30fc\u30bf \u9053","isPartOf":{"@id":"https:\/\/datascientists.info\/#website"},"primaryImageOfPage":{"@id":"https:\/\/datascientists.info\/index.php\/2026\/01\/22\/the-data-engineer-role-in-a-ml-pipeline\/#primaryimage"},"image":{"@id":"https:\/\/datascientists.info\/index.php\/2026\/01\/22\/the-data-engineer-role-in-a-ml-pipeline\/#primaryimage"},"thumbnailUrl":"https:\/\/datascientists.info\/wp-content\/uploads\/2026\/01\/data_engineer_image.png","datePublished":"2026-01-22T13:01:26+00:00","description":"The role of the data engineer is to ensure that the right data is collected, processed, and made available in a reliable and scalable way.","breadcrumb":{"@id":"https:\/\/datascientists.info\/index.php\/2026\/01\/22\/the-data-engineer-role-in-a-ml-pipeline\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/datascientists.info\/index.php\/2026\/01\/22\/the-data-engineer-role-in-a-ml-pipeline\/"]}]},{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/datascientists.info\/index.php\/2026\/01\/22\/the-data-engineer-role-in-a-ml-pipeline\/#primaryimage","url":"https:\/\/datascientists.info\/wp-content\/uploads\/2026\/01\/data_engineer_image.png","contentUrl":"https:\/\/datascientists.info\/wp-content\/uploads\/2026\/01\/data_engineer_image.png","width":1024,"height":1024},{"@type":"BreadcrumbList","@id":"https:\/\/datascientists.info\/index.php\/2026\/01\/22\/the-data-engineer-role-in-a-ml-pipeline\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/datascientists.info\/"},{"@type":"ListItem","position":2,"name":"The Data Engineer Role in a ML Pipeline"}]},{"@type":"WebSite","@id":"https:\/\/datascientists.info\/#website","url":"https:\/\/datascientists.info\/","name":"Data Scientists","description":"Digging data, Big Data, Analysis, Data Mining","publisher":{"@id":"https:\/\/datascientists.info\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/datascientists.info\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-US"},{"@type":"Organization","@id":"https:\/\/datascientists.info\/#organization","name":"DATA DO - \u30c7\u30fc\u30bf \u9053","url":"https:\/\/datascientists.info\/","logo":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/datascientists.info\/#\/schema\/logo\/image\/","url":"https:\/\/datascientists.info\/wp-content\/uploads\/2026\/02\/Bildschirmfoto-vom-2026-02-02-08-13-21.png","contentUrl":"https:\/\/datascientists.info\/wp-content\/uploads\/2026\/02\/Bildschirmfoto-vom-2026-02-02-08-13-21.png","width":250,"height":174,"caption":"DATA DO - \u30c7\u30fc\u30bf \u9053"},"image":{"@id":"https:\/\/datascientists.info\/#\/schema\/logo\/image\/"},"sameAs":["https:\/\/www.facebook.com\/DataScientists\/"]},{"@type":"Person","@id":"https:\/\/datascientists.info\/#\/schema\/person\/723078870bf3135121086d46ebb12f19","name":"Marc Matt","image":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/secure.gravatar.com\/avatar\/74f48ef754cf04f628f42ed117a3f2b42931feeb41a3cca2313b9714a7d4fdd2?s=96&d=mm&r=g53b84b5f47a2156ba8b047d71d6d05fc","url":"https:\/\/secure.gravatar.com\/avatar\/74f48ef754cf04f628f42ed117a3f2b42931feeb41a3cca2313b9714a7d4fdd2?s=96&d=mm&r=g","contentUrl":"https:\/\/secure.gravatar.com\/avatar\/74f48ef754cf04f628f42ed117a3f2b42931feeb41a3cca2313b9714a7d4fdd2?s=96&d=mm&r=g","caption":"Marc Matt"},"description":"Senior Data Architect with 15+ years of experience helping Hamburg's leading enterprises modernize their data infrastructure. I bridge the gap between legacy systems (SAP, Hadoop) and modern AI capabilities. I help clients: Migrate & Modernize: Transitioning on-premise data warehouses to Google Cloud\/AWS to reduce costs and increase agility. Implement GenAI: Building secure RAG (Retrieval-Augmented Generation) pipelines to unlock value from internal knowledge bases using LangChain and Vector DBs. Scale MLOps: Operationalizing machine learning models from PoC to production with Kubernetes and Airflow. Proven track record leading engineering teams.","sameAs":["https:\/\/data-do.de"]}]}},"authors":[{"term_id":144,"user_id":1,"is_guest":0,"slug":"marc","display_name":"Marc Matt","avatar_url":"https:\/\/secure.gravatar.com\/avatar\/74f48ef754cf04f628f42ed117a3f2b42931feeb41a3cca2313b9714a7d4fdd2?s=96&d=mm&r=g","0":null,"1":"","2":"","3":"","4":"","5":"","6":"","7":"","8":""},{"term_id":145,"user_id":2,"is_guest":0,"slug":"saidah","display_name":"Saidah Kafka","avatar_url":"https:\/\/secure.gravatar.com\/avatar\/015737c94dd80772d772f2b24a55e96c868068f28684c8577d9492f3313e4dd3?s=96&d=mm&r=g","0":null,"1":"","2":"","3":"","4":"","5":"","6":"","7":"","8":""}],"_links":{"self":[{"href":"https:\/\/datascientists.info\/index.php\/wp-json\/wp\/v2\/posts\/677","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/datascientists.info\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/datascientists.info\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/datascientists.info\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/datascientists.info\/index.php\/wp-json\/wp\/v2\/comments?post=677"}],"version-history":[{"count":2,"href":"https:\/\/datascientists.info\/index.php\/wp-json\/wp\/v2\/posts\/677\/revisions"}],"predecessor-version":[{"id":696,"href":"https:\/\/datascientists.info\/index.php\/wp-json\/wp\/v2\/posts\/677\/revisions\/696"}],"wp:attachment":[{"href":"https:\/\/datascientists.info\/index.php\/wp-json\/wp\/v2\/media?parent=677"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/datascientists.info\/index.php\/wp-json\/wp\/v2\/categories?post=677"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/datascientists.info\/index.php\/wp-json\/wp\/v2\/tags?post=677"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/datascientists.info\/index.php\/wp-json\/wp\/v2\/ppma_author?post=677"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
![\"The](\"https:\/\/datascientists.info\/wp-content\/uploads\/2026\/01\/data_engineer_image.png\")
<\/figure>\n\n\n\n