Upstaff

Summary

• Strong expertise in Jira and Atlassian ecosystem;
• Audit of current Jira setup and recommendations for improvements;
• Jira customization, reporting and automations;
• Experience with Jira Service Management / Service Desk;
• Ability to independently advise the client and propose solutions, rather than just perform assigned tasks.
   

Job Description

We are looking for a Senior/Expert Jira & Atlassian Consultant with deep expertise in Jira and the broader Atlassian ecosystem. This is not a Jira Administrator role. We are looking for someone who can independently assess the client’s current setup, identify improvement opportunities, and recommend and implement effective solutions.
 

Key Responsibilities

• Audit and assess the existing Jira setup and Atlassian environment
• Identify gaps and opportunities for optimization and improvement
• Provide recommendations on Jira configuration, customization, workflows, reporting, and automation
• Work with Jira Service Management/Service Desk
• Advise the client on best practices and suitable Atlassian solutions
• Independently analyze requirements and propose solutions rather than only executing predefined tasks
• Support the client with Jira/Atlassian-related consulting and technical guidance
   

Requirements

• Deep hands-on expertise with Jira and the Atlassian ecosystem
• Strong experience with Jira customization, workflows, reporting, and automation
• Experience with Jira Service Management/Service Desk
• Strong consulting and problem-solving skills
• Ability to independently communicate with clients, understand their needs, and recommend appropriate solutions
• Experience auditing and optimizing existing Jira environments
   

Engagement

• Global / location-flexible
• Initial engagement expected to be approximately 1–2 months
• Full-time or part-time
• Potential for ongoing support after the initial engagement

We are currently conducting market research and would like to identify consultants who could potentially take on this type of engagement.

Summary

Expert Data Engineer with 3+ years in Power BI Pro, Azure SQL, and complex ETL pipeline development. Skilled in DAX, Power Query, and Azure Synapse notebooks for advanced data analytics and performance tuning. Proven expertise in optimizing Azure Data Lake Storage with Parquet file techniques and managing large-scale backend data processing. Experienced in Agile methodologies, data warehousing, and software engineering best practices, delivering high-quality solutions in cross-functional teams.

Role Overview:
 

The Senior/Expert Data Engineer position is a contract, long-term engagement based in Ukraine, focused on delivering advanced data engineering solutions using Databricks, Power BI, and SQL. The role is embedded within a global consulting and auditing firm supported by a custom product engineering company. The candidate will join a cross-functional development team responsible for engineering innovative data experiences and solving complex business challenges for multinational organizations and scaling startups.

Key Responsibilities:

• Design, develop, and deploy Power BI solutions, including dashboards and reports that provide critical business insights.
• Create and implement robust data models optimized for performance and efficient query execution.
• Optimize Power BI reports and dashboards by improving query speed and data model efficiency.
• Contribute to the design, coding, configuration, and management of data ingestion processes, supporting both near real-time and batch ETL pipelines across multiple data storage systems.
• Perform performance tuning and optimization of complex SQL and DAX queries to enhance data processing efficiency.
• Develop, execute, and maintain data processing workflows using Azure Synapse notebooks.
• Manage and optimize data storage solutions within Azure Data Lake Storage (ADLS), focusing on Parquet file optimization techniques such as partitioning and compression.
• Identify platform gaps and implement improvements to enhance quality, robustness, maintainability, and speed.
• Cross-train team members on emerging technologies and continuously learn from peers to foster knowledge sharing.
• Contribute actively to the unit’s community-building activities, participate in conferences, and uphold best practices and excellence in engineering.

Projects:

The job description does not specify individual project names or dates. However, the candidate’s contributions span multiple projects involving the following contexts and responsibilities:

• Power BI Solution Development: Serving as the lead data engineer responsible for designing and deploying Power BI dashboards and reports, performing advanced data analytics, and implementing complex DAX calculations and Power Query transformations to support business decision-making.
• ETL Pipeline Engineering: Managing and optimizing complex ETL pipelines involving data ingestion, batch processing, and near real-time data flows across diverse data storages, utilizing Azure Data Factory and Azure Synapse Analytics.
• Data Storage Optimization: Overseeing data storage management in Azure Data Lake Storage, applying advanced Parquet file optimization techniques such as partitioning and compression to improve data retrieval and storage efficiency.
• SQL and DAX Performance Tuning: Leading efforts to optimize complicated SQL queries and DAX formulas to enhance report and dashboard performance.
• Azure Synapse Notebook Development: Developing and maintaining data processing workflows within Azure Synapse notebooks to support large-scale backend data processing and analysis systems.

Technologies, Tools, and Methodologies Used:

• Databricks
• Power BI Pro (Desktop and Service)
• DAX (Data Analysis Expressions)
• Power Query
• SQL (Azure SQL Database, Microsoft SQL Server)
• Azure Data Lake Storage (ADLS)
• Parquet file format with optimization techniques (partitioning, compression)
• Azure Synapse Analytics and Notebooks
• Azure Data Factory
• ETL and data integration processes
• Agile software development methodologies, including SCRUM

• Software engineering best practices: coding standards, code reviews, source control, build processes, testing, and operations

   

Additional Contributions and Skills:

• Strong problem-solving and analytical skills with meticulous attention to detail.
• Excellent communication skills, capable of presenting complex data clearly and concisely to stakeholders at various organizational levels.
• Experience collaborating within cross-functional teams and contributing to community-building and knowledge sharing.
• Demonstrated ability to learn new technologies quickly and independently.
• Proven track record of delivering high-quality software on large, complex, cross-functional projects.

Context

We are building a complex agent that analyzes documents and produces defined data structures extracted from them. Three properties are critical: the structures must be accurate — faithful to the source documents, with exact references and exact quotes; valid — types conforming to their schemas, required fields filled, and so on; and free of duplicates — not repeating structures that were already accepted or rejected before.

Must-have

- 3+ shipped LLM-powered systems in production, each with a measurable quality harness the candidate built personally (golden sets, precision/recall tracking, regression gates in CI). Be ready to walk through one in depth — at whatever level your own NDAs allow — including a concrete regression the harness caught and what changed because of it.
- Proven ability to split model work from deterministic validation: pipelines where extraction is model-driven but every acceptance check (dedup, linkage, schema, rule-conformance) is executable code. Prompt-only quality control is a disqualifier.
- Information-retrieval and data-modeling depth: hybrid exact+semantic indexing; identity-keyed deduplication (record identity over embedding similarity); append-only stores with supersession semantics; incremental re-indexing without full rebuilds.
- Multi-pass agent-pipeline engineering: idempotent, sumable fan-out/verify loops (extract -> validate -> dedup/link -> adversarial verify -> loop-until-dry), token/cost budgeting, caching.
- Evidence-first design method: for every architectural element you propose, you state its precondition and how it is verified; you back your designs with executable acceptance checks against golden sets, not documents alone.

Nice-to-have

- Experience working with and analyzing regulated-domain documents (insurance/finance/legal).
- Event-sourced or lakehouse-style record stores (append-only, versioned, time-travel).
- Knowledge-graph construction and record linkage / entity resolution.
- Adversarial-verification agent patterns (a dedicated verifier stage that tries to refute extractions).
- Custom eval-suite tooling built when off-the-shelf eval frameworks did not fit.

Summary

• Senior AI/ML Engineer with extensive experience deploying machine learning models on cloud platforms including GCP, AWS, and Azure.
• Experienced in Python and ML frameworks such as TensorFlow, PyTorch, and scikit-learn.
• CI/CD pipelines and managing ML workflows: Kubeflow and VertexAI.
• Background in data processing with Apache Beam/Dataflow and expertise in model monitoring, performance optimization, and scaling.
• Ability to collaborate cross-functionally to deliver AI-driven solutions in production environments.

Location: Poland, Romania, Bulgaria, Czech Republic, Slovakia.

• Location: Poland - B2B Only
• Romania - COE or B2B,
• Bulgaria: COE / B2B,
• Czech Republic - CEO
• Slovakia - COE

Role Overview:

The Senior Artificial Intelligence/Machine Learning Engineer is a key member of the MLOps team within a cross-functional development environment. This role involves close collaboration with data scientists, software engineers, and other stakeholders to operationalize machine learning models by deploying, maintaining, and monitoring them efficiently in production. The engineer contributes to enhancing model performance and infrastructure, supporting AI-driven solutions that address complex business challenges.

Responsibilities:

• Model Deployment: Collaborate with data scientists to transition machine learning models from development to production, ensuring high performance and scalability.
• Build Pipelines: Develop and maintain data and model pipelines that integrate seamlessly with existing systems to support reliable and efficient workflows.
• CI/CD for ML: Design and implement continuous integration and delivery pipelines to streamline the deployment process of machine learning models.
• Model Monitoring: Monitor the performance of deployed machine learning models to ensure reliability, scalability, and quality over time.
• Collaboration: Work with cross-functional teams to design solutions that meet business needs while adhering to best practices in machine learning and software engineering.
• Optimization: Continuously improve infrastructure to maintain cutting-edge AI model production and delivery capabilities.

Technologies and Tools:

• Cloud platforms: Google Cloud Platform (GCP), Amazon Web Services (AWS), Microsoft Azure
• CI/CD tools: GitHub Actions, Docker
• ML platforms/frameworks: VertexAI (Enterprise Agent Platform), Kubeflow, SageMaker
• Data processing frameworks: Apache Beam, Dataflow (highly desirable)
• Programming languages and libraries: Python, TensorFlow, PyTorch, scikit-learn

Key Skills and Competencies:

• Strong foundation in both machine learning and software engineering
• Experience deploying machine learning models in production environments
• Expertise in building and maintaining CI/CD pipelines for ML
• Knowledge of monitoring and maintaining models post-deployment
• Problem-solving and troubleshooting skills related to models and pipelines
• Performance and cost optimization experience (e.g., latency, throughput) is highly desirable
• Excellent communication skills for effective cross-team collaboration

Technologies: Cloud platforms (GCP, AWS, Azure), VertexAI, Kubeflow, SageMaker, Python, TensorFlow, PyTorch, scikit-learn, Apache Beam, Dataflow, Docker, CI/CD, GitHub Actions.

Our client is a proven Nordic alarm and event handling software company specializing in Personal Care, Personal Security, and IoT. We are looking for a skilled Full-stack Developer passionate about building reliable, scalable software solutions. If you enjoy working with modern technologies, solving complex integration challenges, and taking ownership of your work, this role is for you!
In this role, you will collaborate directly with the development team on the client’s side, actively participating in all phases of the software development lifecycle.

What you’ll do in this role

• Develop and maintain full-stack applications using C#/.NET and React
• Design and implement scalable cloud-native solutions on AWS
• Build new features and improve existing functionality across the platform
• Collaborate with product managers, designers, and engineers in an agile environment
• Optimize application performance, reliability, and security
• Write clean, maintainable, and well-tested code
• Contribute to architecture discussions and technical decisions
• Explore and leverage AI-assisted development tools to improve engineering productivity

We’re looking for

• 4+ years of commercial experience with C#/.NET
• Hands-on experience with React
• Experience working with AWS and cloud-based applications
• Solid knowledge of SQL and relational databases
• Experience designing and consuming REST APIs
• Understanding of modern software engineering practices and Agile development
• Upper-Intermediate or higher English level

Summary

• Engineer specializing in data migration and engineering with extensive experience in SAP BW architecture, data modeling, and business logic transformation.
• Experienced in designing and developing scalable data pipelines using Databricks and AWS cloud platforms.
• Skilled in leveraging LLM-based tools for automated code conversion and migration, ensuring data integrity and consistency.
• Demonstrated success in large-scale enterprise projects, particularly within finance domains, with collaboration across business and technical teams.

Job DescriptionRole Overview

The Data Engineer with SAP BW is responsible for reviewing and migrating existing SAP BW architectures, data models, and business logic to a modern data platform using Databricks. This role requires strong expertise in data engineering, data modeling, and cloud technologies, with a focus on ensuring data accuracy, integrity, and consistency throughout the migration process by utilizing automated and LLM-based code conversion tools to enhance efficiency. The engineer will collaborate closely with both business and technical teams to validate the migrated datasets and transformations, supporting the overall solution architecture and migration strategy.

Responsibilities

• Review current SAP BW architecture, data models, and business logic to determine the scope of the migration.
• Design and develop target data models and data pipelines within Databricks.
• Transfer and transform data and business logic from SAP BW to Databricks while maintaining data accuracy, integrity, and consistency.
• Support the overall solution architecture and migration strategy, including leveraging automated or LLM-based code conversion tools where appropriate.
• Work closely with business and technical teams to validate migrated datasets, transformations, and business logic.

Skills and Qualifications

• Strong hands-on experience with Databricks.
• Proven track record in delivering data migration projects, preferably in large-scale or enterprise environments.
• Practical experience with SAP BW, including understanding of its data structures, models, and business logic.
• Solid background in data modeling, business intelligence, and data engineering.
• Strong experience with AWS or similar cloud platforms.
• Experience using LLM-based tools for code translation, transformation, or migration activities.
• Strong English communication skills.

Additional Desirable Experience

• Previous experience in the finance domain.
• Experience working within large enterprise organizations.

Technologies and Tools: SAP BW, Databricks, AWS (or similar cloud platform), LLM-based tools for code translation and migration, data modeling and BI tools.