DataArt

Client

Our client is a large international manufacturing company listed in the S&P 500, operating a complex, multi-site global environment. As part of a Data Strategy Development initiative, the organization is defining a clear, pragmatic, and scalable data strategy to improve decision-making, reduce manual processes, strengthen data ownership, and establish a strong foundation for future analytics and digital transformation. The engagement focuses on strategy definition and is expected to continue into platform and analytics implementation phases.

Project overview

This initiative focuses on establishing a robust, enterprise-wide data strategy and building a data-driven culture. The project involves standing up a modern target operating model, deploying advanced data platforms, and creating the data leadership architecture necessary to support long-term business capabilities.

Position overview

The Data Consultant will lead the people and process aspects of the organization's data strategy, specifically driving data governance, change management, and the data literacy agenda. Serving as a business-facing leader, this role acts as the primary bridge between business stakeholders, executive sponsors, and the technical delivery teams. The ideal candidate possesses strong business acumen paired with sufficient technical literacy to maintain credibility with data architecture teams and ensure seamless strategic alignment.

Responsibilities

• Data Governance: Lead the implementation of the data governance blueprint, including the operating model, defining roles (data owners and stewards), establishing policies and standards, and facilitating and sustaining governance forums.
• Change Management: Own and execute the comprehensive change management plan, covering stakeholder engagement, communications, user adoption, and the transition to the target operating model.
• Data Leadership: Drive the data leadership and data literacy agenda to foster a mature, data-driven organizational culture and build internal capabilities.
• Stakeholder Partnership: Collaborate directly with business and functional leaders to capture requirements, prioritize data use cases, and secure organizational buy-in.
• Executive Alignment: Maintain alignment across executive sponsors, the Data Architect, and change program leads regarding project scope, priorities, risks, and milestones.
• Technical Alignment: Ensure all governance processes and adoption strategies are technically grounded by maintaining a strong baseline understanding of the deployed data platforms and architecture.
• Artifact Ownership: Author and quality-assure core strategic artifacts, including governance blueprints, operating models, and change plans tailored for executive audiences.

Requirements

• Proven track record of leading data governance and/or data management programs within a complex enterprise environment.
• Strong change management capability, with demonstrated experience in stakeholder engagement, communication strategy, and operating-model transitions.
• Proven ability to effectively engage, collaborate with, and influence senior and C-level stakeholders.
• Sound technical literacy regarding data platforms and analytics, enabling close partnership with architects and engineers.
• Exceptional communication and facilitation skills, with a verified ability to translate complex concepts between business and technical audiences.

Nice to have

• Experience in manufacturing or industrial enterprise environments.
• Background in large-scale data or digital transformation programs.
• Familiarity with Azure, Snowflake, and modern cloud data platforms.
• Experience contributing to executive-level strategy reports and board-level materials.
• Consulting or principal-level advisory experience in global organizations.

Client

Our client is an international SaaS company developing tools that empower businesses to create highly engaging digital experiences. Their solutions are trusted by thousands of organizations worldwide to collect, analyze, and act on user data effectively.

Project overview

The project is an interactive form builder designed for marketers, researchers, HR, sales, and customer experience teams to create engaging, conversational online forms, surveys, and quizzes.

Key features:
Visually appealing conversational design optimized for high completion rates
AI-powered data interpretation
Workflow and logic builders for personalized experiences
Brand customization options
Seamless integrations with various tools to automate data collection and streamline workflows

Position overview

We are seeking a Full Stack Engineer who builds with AI in the loop — both as a daily development practice and as a product capability. You are fluent with coding agents (Claude Code, Cursor, Copilot) and ship faster because of them, not despite them. You understand how to design, prompt, evaluate, and operate LLM-backed features in production — including model selection, context engineering, evals, guardrails, and cost and latency tradeoffs.

Beyond AI, you have deep Golang and React/TypeScript expertise and own features end-to-end: from understanding business requirements, through writing and testing code, to deployment and production monitoring. This role requires strong autonomy, proactive communication, and the ability to deliver reliable, scalable solutions in a modern cloud environment.

This position operates according to the CET time zone.

Responsibilities

• Design and ship LLM-powered product features: prompt design, retrieval, tool and function calling, structured outputs, streaming, evaluation harnesses, and guardrails.
• Operate AI features in production: monitor quality, latency, cost, and failure modes; iterate on prompts, models, and architectures based on real usage.
• Use AI coding tools (Claude Code, Cursor, Copilot) as a core part of your workflow to accelerate delivery without compromising code quality.
• Design, develop, and maintain backend services using Golang and frontend applications using React/TypeScript.
• Collaborate with product managers, designers, and other engineers to translate business requirements into technical solutions.
• Implement event-driven architectures leveraging Kafka for real-time data processing.
• Manage and optimize AWS environments, ensuring scalability and reliability.
• Define and maintain infrastructure using Terraform and deploy services in Kubernetes clusters.
• Build and maintain CI/CD pipelines in Jenkins for automated deployments.
• Ensure high-quality code through comprehensive testing and code reviews.
• Monitor application performance and troubleshoot issues in production environments.
• Contribute to UI/UX improvements and ensure seamless integration between frontend and backend.

Requirements

• Demonstrable production experience shipping LLM-powered features. Be ready to walk through a feature you built: prompts, evaluation approach, failure modes you fixed, and how you measured success.
• Hands-on with at least one major LLM provider's API (Anthropic, OpenAI, AWS Bedrock, or equivalent), including streaming, tool use, and structured outputs.
• Daily use of AI coding assistants in your workflow; able to articulate where they help, where they hurt, and how you maintain code quality.
• Strong, in-depth knowledge of Golang.
• Experience delivering complex backend systems from design to production.
• Solid background with Kafka and event-driven architectures.
• Hands-on experience managing AWS environments.
• Proficiency with Terraform for infrastructure-as-code.
• Experience deploying and operating services in Kubernetes.
• Building CI/CD pipelines in Jenkins.
• Proven ability to ensure quality through comprehensive testing.
• Strong proficiency in React and TypeScript.
• Experience contributing to front-end features, UI components, and client-side logic.
• Ability to seamlessly integrate frontend with backend services.
• Excellent English communication skills, written and spoken.
• Self-driven and able to work independently while managing workload effectively.

Nice to have

• Experience with agent frameworks, multi-step tool-use loops, or RAG systems at scale.
• Built or maintained an evaluation pipeline (offline evals, LLM-as-judge, human review loops).
• Open-source contributions, published writing, or talks on applied AI engineering.

Project overview

This project focuses on building and enhancing a mature cyber defense capability that combines advanced incident response with security engineering and automation. The goal is to improve detection accuracy, accelerate response times, and strengthen protection across cloud and on premise systems.

Position overview

This role serves as the last escalation point for Incident Management & Incident Response within the SOC team. The engineer is engaged when L2 analysts face technically complex incidents — including malware analysis, digital forensics, high-impact events, and incidents with significant business or client exposure. In addition to L3 analytical work, the role covers engineering responsibilities: SIEM fine-tuning, SOAR automation, data source onboarding, and quality control of incident tickets. The engineer also mentors junior analysts and drives team knowledge-sharing.

Responsibilities

• Act as the final escalation point for complex security incidents and lead end to end investigations
• Perform malware triage and behavioral analysis using both static and dynamic techniques
• Conduct digital forensics activities including evidence collection, artifact analysis, and timeline reconstruction
• Develop and tune detection rules in Microsoft Sentinel and improve analytics and correlation logic
• Write and optimize KQL queries to enhance detection quality and reduce false positives
• Configure and maintain data connectors, parsers, and normalization pipelines
• Design and improve SOAR playbooks to automate triage and response workflows
• Work with FortiAnalyzer to manage log collection, correlation rules, and alerting pipelines
• Collaborate with international teams to ensure consistent security monitoring practices
• Review incident tickets and ensure quality and completeness of investigations
• Mentor L1 and L2 analysts and contribute to knowledge sharing initiatives
• Break down engineering tasks and support junior team members in their execution

Requirements

• 3 to 5 years of experience in a SOC environment at L2 or Security Engineer level
• Experience acting as an escalation point for incident response and managing complex investigations end to end
• Hands on experience with malware analysis including identification of indicators of compromise and behavior analysis
• Experience with digital forensics fundamentals including evidence handling and timeline reconstruction
• Practical experience with Microsoft Sentinel including rule creation, tuning, and data onboarding
• Strong KQL skills with the ability to build and optimize analytical queries
• Experience with Microsoft Defender XDR products and advanced hunting capabilities
• Familiarity with Microsoft Azure security services including Entra ID and monitoring tools
• Experience working with AWS security services such as CloudTrail, GuardDuty, and Security Hub
• Experience with Fortinet FortiAnalyzer for log management and correlation
• Understanding of networking, operating systems, and enterprise security principles
• Experience with SOAR platforms and automation of incident response workflows
• Experience mentoring team members and supporting knowledge development

Nice to have

• Experience with AI driven SOC workflows and automation using language models
• Familiarity with Microsoft Copilot Studio or low code automation tools such as Logic Apps
• Experience integrating threat intelligence platforms and enrichment pipelines into SOC processes

Our client is a prominent global wealth and asset management firm focused on delivering innovative investment solutions and a networked ecosystem of international opportunities. The organization operates within a highly regulated environment, prioritizing the transformation of complex concepts into actionable financial strategies for a global clientele.

Project overview

The project involves the development and expansion of a centralized, scalable data ecosystem. Having successfully ingested the initial data source, the team is now moving into a high-growth phase to integrate multiple high-value financial and operational data streams. The goal is to provide C-level executives and advisors with a unified, reliable reporting layer for strategic decision-making.

Position overview

As a Senior Data Engineer, you will be a technical cornerstone of the data platform team. You will be responsible for the end-to-end engineering of data pipelines, from ingestion to transformation, ensuring that the Snowflake warehouse is optimized for complex wealth management analytics. You will work at the intersection of sophisticated financial data and enterprise-grade engineering.

Responsibilities

• Build and maintain scalable data pipelines for ingestion into Snowflake.
• Develop and manage transformations using dbt, including modeling, testing, and comprehensive documentation.
• Design and orchestrate complex data workflows using Prefect.
• Utilize Workato to integrate and normalize data from critical SaaS platforms, including Addepar, NetSuite, Salesforce, Morningstar, and UKG.
• Design specialized data models tailored for portfolio, client, and financial reporting.
• Ensure data quality, lineage, and governance best practices while optimizing performance and costs within Snowflake.
• Partner with Tableau developers and business stakeholders to align technical delivery with reporting needs.

Requirements

• 5+ years of experience in data engineering with a focus on building modern data platforms.
• Hands-on experience with Snowflake and dbt, supported by strong SQL skills and advanced data modeling expertise.
• DBT certification.
• Proven familiarity with ETL/ELT patterns and API-based ingestion.
• Practical experience with orchestration tools such as Prefect or Airflow.
• A solid understanding of financial or wealth management data structures.

Nice to have

• Experience supporting reporting requirements for Tableau-based visualization layers.
• Experience within the Wealth Management or Investment Management sector, with specific familiarity handling investment and portfolio-related data.
• Understanding of regulatory requirements regarding financial data handling and privacy.
• Background in integrating market data providers such as Bloomberg or Morningstar.
• Advanced SQL skills and experience with Python for data engineering tasks.