Ollion

Specialist Engineer- Data

Rol remoto de Data Engineering con fit claro de ubicación del candidato.

Publicado21 jun 2026

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Azure CI/CD GCP Java Python Snowflake SQL

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Actualidad de la fuente21 jun 2026

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Match de stackAzure, CI/CD

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Resumen del rol

Specialist Engineer- Data

Requisitos y responsabilidades

Contenido del rol extraído en secciones para revisar más rápido.

Job Requirements:

Design and Development: Design, build, and optimize robust and scalable ETL/ELT data pipelines to ingest and process large volumes of data using Azure and GCP services (e.g., Azure Data Factory, Azure Synapse/Databricks, GCP Dataflow, Cloud Data Fusion, or Cloud Functions).
Data Modeling and Warehousing: Develop and maintain optimized data models (e.g., dimensional, vault) within multi-cloud data warehouse solutions (e.g., Google BigQuery or Azure Synapse Analytics) or data lakes to support BI, reporting, and analytical workloads. This includes ensuring data structures are optimized for consumption by tools like Power BI and Looker.
Performance Tuning: Monitor, troubleshoot, and optimize the performance of data warehouse queries and compute resources (e.g., BigQuery slots, Azure Synapse SQL pools, or Databricks/Dataproc clusters) to ensure cost-efficiency and fast data retrieval.
AI Data Foundation:Feature Engineering: Collaborate with Data Scientists to design and implement feature stores and pipelines to prepare and serve data for ML model training and inference.Vector Database Integration: Develop and maintain pipelines for transforming unstructured data (text, documents) into embeddings and loading them into vector databases (e.g., Azure Cosmos DB, GCP Vertex AI Vector Search, or dedicated vector stores) to support RAG solutions.Data Orchestration: Implement workflows (e.g., using Apache Airflow, Google Cloud Composer, or Azure Data Factory/Logic Apps) to automate the end-to-end data lifecycle for AI/ML processes, including data refresh and model retraining.
Feature Engineering: Collaborate with Data Scientists to design and implement feature stores and pipelines to prepare and serve data for ML model training and inference.
Vector Database Integration: Develop and maintain pipelines for transforming unstructured data (text, documents) into embeddings and loading them into vector databases (e.g., Azure Cosmos DB, GCP Vertex AI Vector Search, or dedicated vector stores) to support RAG solutions.
Data Orchestration: Implement workflows (e.g., using Apache Airflow, Google Cloud Composer, or Azure Data Factory/Logic Apps) to automate the end-to-end data lifecycle for AI/ML processes, including data refresh and model retraining.

AI Data Foundation:

Feature Engineering: Collaborate with Data Scientists to design and implement feature stores and pipelines to prepare and serve data for ML model training and inference.
Vector Database Integration: Develop and maintain pipelines for transforming unstructured data (text, documents) into embeddings and loading them into vector databases (e.g., Azure Cosmos DB, GCP Vertex AI Vector Search, or dedicated vector stores) to support RAG solutions.
Data Orchestration: Implement workflows (e.g., using Apache Airflow, Google Cloud Composer, or Azure Data Factory/Logic Apps) to automate the end-to-end data lifecycle for AI/ML processes, including data refresh and model retraining.

MLOps and Productionization:

Model Deployment: Work with Data Science teams to containerize, deploy, and manage machine learning models in production environments (e.g., using GCP Vertex AI, Azure Machine Learning, or AKS/GKE).
Monitoring and Logging: Implement robust monitoring and logging solutions for production ML pipelines and models to track performance, data drift, and model decay using Azure Monitor or GCP Cloud Monitoring.
CI/CD for ML: Integrate model training, testing, and deployment into CI/CD pipelines to ensure rapid, reliable, and automated updates to production ML services.
Security and Governance: Implement and manage security best practices across Azure, GCP, and Snowflake, including access controls, role-based security (RBAC), IAM policies, and data encryption.
Coding and Automation: Write complex, efficient SQL queries and develop scripts in Python (or other relevant languages like Scala/Java) for data manipulation, process automation, and pipeline orchestration.
Collaboration: Work closely with data analysts, data scientists, and business stakeholders to understand data requirements and deliver high-quality, actionable data solutions, including setting up data sources and datasets for BI tools like Power BI and Looker.
Documentation: Create and maintain technical documentation for data models, data flows, and ETL/ELT processes.

Expertise You Bring:

Cloud Data Platform Mastery: hands-on experience designing and operating data solutions using major Azure services (Blob Storage, Data Factory, Databricks, Synapse) and/or GCP services (Cloud Storage, BigQuery, Dataflow, Pub/Sub, IAM).
Transformation & Modeling: Expert in SQL and modern ELT methodologies using tools like dbt (Data Build Tool) for version-controlled, production-grade data modeling within a modern cloud data warehouse (e.g., BigQuery, Azure Synapse, or Snowflake).
BI & Reporting: Experience with BI tools, specifically the configuration and optimization of data for use in Power BI or Looker.
Engineering Excellence: Advanced proficiency in Python for complex data transformation, API integrations, and automation scripting. Experience with workflow orchestration tools (Apache Airflow, Google Cloud Composer, or Azure Data Factory).
Machine Learning Engineering & MLOps:Proven experience working with Data Scientists to build data and feature pipelines for ML.Familiarity with ML lifecycle tools and frameworks (e.g., GCP Vertex AI, Azure Machine Learning, Kubeflow, MLflow).Understanding of machine learning model deployment, serving, monitoring, and versioning best practices.
Proven experience working with Data Scientists to build data and feature pipelines for ML.
Familiarity with ML lifecycle tools and frameworks (e.g., GCP Vertex AI, Azure Machine Learning, Kubeflow, MLflow).
Understanding of machine learning model deployment, serving, monitoring, and versioning best practices.
Data Security & Governance: Expertise in applying security controls, including encryption, data masking, and implementing role-based access control (RBAC) models in Azure and GCP data services.
DevOps Capabilities: Familiarity with infrastructure as code (IaC) practices using Terraform (preferred for multi-cloud) or cloud-native tooling (Azure Bicep / GCP Deployment Manager) and experience with version control, CI/CD pipelines, and automation tools for cloud data services.
Education & Experience: Bachelor’s or Master’s degree in Computer Science, Engineering, or a quantitative field. 4+ years of professional experience in a Data Engineering, Software Engineering, or Data Architecture role.
Required Certifications: Must hold professional-level data/AI certifications in at least one of the major platforms, such as:Azure: Microsoft Certified: Azure Data Engineer Associate, or Azure AI Engineer Associate.GCP: Google Cloud Certified Professional Data Engineer, or Professional Machine Learning Engineer.
Azure: Microsoft Certified: Azure Data Engineer Associate, or Azure AI Engineer Associate.
GCP: Google Cloud Certified Professional Data Engineer, or Professional Machine Learning Engineer.

Machine Learning Engineering & MLOps:

Proven experience working with Data Scientists to build data and feature pipelines for ML.
Familiarity with ML lifecycle tools and frameworks (e.g., GCP Vertex AI, Azure Machine Learning, Kubeflow, MLflow).
Understanding of machine learning model deployment, serving, monitoring, and versioning best practices.

Details

Azure: Microsoft Certified: Azure Data Engineer Associate, or Azure AI Engineer Associate.
GCP: Google Cloud Certified Professional Data Engineer, or Professional Machine Learning Engineer.

BENEFITS & PERKS FOR WORKING AT OLLION

Benchmarked, competitive, in-market total rewards package including (but not limited to): base salary & short-term incentive for all employees
Fully remote-first, small but Global organization; ‘learn wherever, whenever’ frees our people from a rigid view of learning and growth
Retiral benefits which are as per the local statutory regulations as our company is 100% compliant
Globally, we build benefit plans that offer choices for whatever stage in life our employees are in and allow for flexibility as life happens.
Employees have the benefit of medical insurance which is in line with industry benchmark.
In addition to great healthcare coverage, we also offer all employees mental health resources and additional wellness programs.
Generous time off and compensatory off
And more!

DIVERSITY AT OLLION

Awareness and sensitisation programs: to create awareness and sensitisation. We encourage open dialogue, active listening, and mutual respect, creating a safe and supportive environment for everyone to contribute their unique perspectives and ideas.
Dedicated efforts to building diverse teams: that leverage the strength of our differences to tackle complex challenges and drive innovation. By embracing diversity, we broaden our collective knowledge, enhance problem-solving capabilities, and unlock limitless potential for our employees.

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