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Remote Big Data Engineer Jobs in Wisconsin (NOW HIRING)

Qualysoft

Der Fokus liegt auf Big-Data-Engineering , ML/LLM-Workloads , MLOps-Automatisierung sowie der ... Hybrid - ca. 50 % vor Ort in Wien, ca. 50 % remote Projektsprache: Deutsch und Englisch Aufgaben: ...

Blain's Farm & Fleet

IT Data Engineer

Janesville, WI · On-site +1

$72K - $90K/yr

Job Duties As a Data Engineer, you will play a key role in designing, implementing, and maintaining our data integrations, storage and access solutions. You will work closely with cross-functional ...

Cloudera

$171K - $210K/yr

Big Data, Data Warehousing or Relational Databases * One or more of the three major cloud service ... Employee Resource Groups EEO/VEVRAA #LI-MH2 #LI-Remote

Hikinex

REMOTE OR HYBRID IS POSSIBLE FOR THE RIGHT CANDIDATE. Open for candidates in Canada as well as the ... Big Data & Analytics: • Experience handling large telemetry datasets from satellites and ground ...

Qualysoft

Remote bevorzugt, gelegentliche Vor-Ort-Termine nach Absprache 400 - 450 a day Aufgaben Aufbau und ... Betrieb von End-to-End Data-Science- und ML-Workflows Implementierung von ML ...

Cielo

Senior Director - Client Data Solutions

Wauwatosa, WI · Remote

$103K - $139K/yr

Cielo is a brand that reflects our big idea - that talent is rising - and with it our opportunity ... Remote - United States Work set up: Monday to Friday - FTE Skills: Are you Strong experience in ...

Cielo

Senior Director - Client Data Solutions

Wauwatosa, WI · On-site +1

$103K - $139K/yr

Cielo is a brand that reflects our big idea - that talent is rising - and with it our opportunity ... Remote - United States Work set up: Monday to Friday - FTE Skills: Are you Strong experience in ...

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Remote Big Data Engineer information

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How much do remote big data engineer jobs pay per hour?

As of Jul 14, 2026, the average hourly pay for remote big data engineer in Wisconsin is $63.57, according to ZipRecruiter salary data. Most workers in this role earn between $54.09 and $71.59 per hour, depending on experience, location, and employer.

What is a Remote Big Data Engineer job?

A Remote Big Data Engineer designs, develops, and manages large-scale data processing systems while working from a remote location. They build data pipelines, optimize data storage, and ensure efficient data processing for analytics and machine learning applications. Their role involves working with technologies like Hadoop, Spark, and cloud platforms to handle massive datasets. Effective communication and collaboration with distributed teams are essential for success in this role.

What are typical challenges faced by Remote Big Data Engineers, and how can they be managed?

Remote Big Data Engineers often navigate challenges such as collaborating across different time zones, ensuring secure and efficient data transfer, and maintaining clear communication with distributed teams. Staying up to date with rapidly evolving technologies and troubleshooting complex data pipeline issues without in-person support can also be demanding. Successful engineers manage these challenges by leveraging robust project management tools, fostering transparent communication, and participating in ongoing training or knowledge-sharing sessions. By proactively addressing these hurdles, they contribute to smoother project delivery and continuous team collaboration.

What are the key skills and qualifications needed to thrive in the Remote Big Data Engineer position, and why are they important?

To thrive as a Remote Big Data Engineer, you need a strong background in computer science, data engineering, and programming languages such as Python, Java, or Scala, often supported by a relevant degree. Expertise in big data technologies like Hadoop, Spark, Kafka, and cloud platforms (AWS, Azure, or GCP), along with certifications like Google Professional Data Engineer or AWS Certified Big Data, is highly valuable. Exceptional problem-solving abilities, self-motivation, and effective remote communication skills allow you to excel in distributed teams. These skills and qualities enable you to efficiently manage, analyze, and derive insights from massive datasets in a remote work environment.

What are the most commonly searched types of Big Data Engineer jobs in Wisconsin? The most popular types of Big Data Engineer jobs in Wisconsin are:
What are popular job titles related to Remote Big Data Engineer jobs in Wisconsin? For Remote Big Data Engineer jobs in Wisconsin, the most frequently searched job titles are:
What job categories do people searching Remote Big Data Engineer jobs in Wisconsin look for? The top searched job categories for Remote Big Data Engineer jobs in Wisconsin are:
What cities in Wisconsin are hiring for Remote Big Data Engineer jobs? Cities in Wisconsin with the most Remote Big Data Engineer job openings:
Remote Big Data Engineer jobs near you
Infographic showing various Remote Big Data Engineer job openings in Wisconsin as of July 2026, with employment types broken down into 2% As Needed, 69% Full Time, 26% Part Time, 1% Temporary, and 2% Contract. Highlights an 95% Physical, 1% Hybrid, and 4% Remote job distribution, with an average salary of $132,226 per year, or $63.6 per hour.

Databricks AI / ML Engineer (m/w/d)

Qualysoft

On-site, Remote

Contractor

Re-posted 11 days ago

Job description

Fur ein langfristig angelegtes Daten- und KI-Programm wird ein Databricks AI / ML Engineer (m/w/d) gesucht.
Ziel ist die Entwicklung, Implementierung und der Betrieb skalierbarer Machine-Learning- und LLM-Losungen auf Azure Databricks von der Datenaufbereitung uber Feature Engineering bis hin zu MLOps, Deployment und Monitoring.
Der Fokus liegt auf Big-Data-Engineering, ML/LLM-Workloads, MLOps-Automatisierung sowie der nahtlosen Integration in das Microsoft-Okosystem.
520 - 560 a day
Rahmenbedingungen
Start: Marz/April 2026
Laufzeit: 3 Jahre (optional verlangerbar bis max. 5 Jahre)
Auslastung: 100 %
Arbeitsmodell: Hybrid - ca. 50 % vor Ort in Wien, ca. 50 % remote
Projektsprache: Deutsch und Englisch

Aufgaben:
Datenanalyse und Prototyping mit Python in Azure Databricks unter Einsatz gangiger ML-Frameworks
Entwicklung und Betrieb von Big-Data-Pipelines mit Apache Spark, Delta Lake und Databricks SQL
Durchfuhrung von Feature Engineering sowie Training, Versionierung und Deployment von Modellen mit Databricks MLflow
Entwicklung und Betrieb von ML- und LLM-Workloads auf Azure Databricks (inkl. Unity Catalog, Performance- und Kostenoptimierung)
End-to-End-Integration der Losungen in das Microsoft-Okosystem (z. B. API- und Schnittstellendesign, Orchestrierung mit Azure Functions und Logic Apps)
Aufbau und Weiterentwicklung von MLOps- und CI/CD-Pipelines fur automatisiertes Training, Testing, Deployment und Monitoring von ML- und LLM-Modellen sowie Agents
Durchfuhrung von Modell- und Datenmonitoring (Modellleistung, Daten-Drift, Bias) inklusive Wartungs- und Updateprozessen
Einsatz von AutoML-Tools zur Beschleunigung von Prototypen und Experimenten
Sicherstellung von Skalierbarkeit, Sicherheit und stabilen Betriebsprozessen der entwickelten Losungen

Fachliche Anforderungen:
Fundierte Kenntnisse in Datenanalyse und Prototyping mit Python in Azure Databricks
Erfahrung mit Machine-Learning-Frameworks wie TensorFlow, PyTorch und scikit-learn
Praktische Erfahrung im Big Data Engineering mit Apache Spark, Delta Lake und Databricks SQL
Kompetenz in Feature Engineering sowie Modell-Deployment mit managed Databricks MLflow
Erfahrung in der Entwicklung und dem Betrieb von ML- und LLM-Workloads auf Azure Databricks
Erfahrung mit End-to-End-Integrationen im Microsoft-Okosystem
Erfahrung im Aufbau von MLOps- und CI/CD-Pipelines fur ML- und LLM-Modelle sowie agentische Workflows
Erfahrung im Modell- und Datenmonitoring (Leistung, Drift, Bias) inklusive passender Wartungsstrategien
Praktische Erfahrung im Einsatz von AutoML-Tools
Vorhandensein einer eigenen, vom Produktivsystem des Auftraggebers getrennten Entwicklungsumgebung, die den aktuellen Standards fur Datensicherheit und Zugriffsschutz entspricht (inkl. Nachweis der Infrastruktur)

PLUS:
Erfahrung mit Data- und KI-Governance
Erfahrung in der Konzeption und Umsetzung agentischer Ansatze (Agenten, Multi-Agent-Systeme, agentische Workflows) mit Azure-Ressourcen
Erfahrung in der Umsetzung von End-to-End-Databricks-Projekten (von Datenaufbereitung und Feature Engineering uber Modelltraining und Deployment bis zu MLOps und Monitoring)
Branchenkenntnisse in der Energieindustrie
Strukturierte und analytische Arbeitsweise
Hohes Qualitatsbewusstsein und Verantwortungsbereitschaft
Sehr gute Kommunikationsfahigkeit gegenuber technischen und fachlichen Stakeholdern
Teamfahigkeit und Bereitschaft zur Wissensweitergabe

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