Data Engineering at Alldus

Job description

Data Science Data Engineering Machine Learning ServiceNow - Consultant ServiceNow Developer ServiceNow Business Analyst Data Science Data Engineering Machine Learning ServiceNow - Consultant ServiceNow Developer ServiceNow Business Analyst filters Filters Specialism Country Location Job Type Categories Specialism Country Location Job Type Founding Data Engineer San Francisco, California Data Engineering Permanent Learn More Apply Now Our client, a growing AI and Data organization, is hiring a Founding Data Engineer to join the team. The successful candidate will help transform enterprise data into actionable intelligence by building the backend systems, data pipelines, connector frameworks and graph-based knowledge models that power their Agentic AI applications. Responsibilities • Build reliable, scalable data ingestion and transformation pipelines across structured, semi-structured and unstructured data sources. • Develop and maintain connector frameworks that integrate enterprise systems, including ERPs, PLMs, CRMs, legacy platforms, email, Excel, documents and web APIs. • Design and enhance the Data Fabric layer, including knowledge graphs enriched with ontologies, metadata and relationships. • Prepare, enrich, normalize and vectorize enterprise data for AI and LLM applications, including RAG, summarization and intelligent alerting. • Establish and maintain data contracts, access layers, lineage, governance and data quality processes. • Develop secure APIs that enable services, AI agents and users to access and query enriched semantic data. • Partner with ML and LLM teams to provide high-quality enterprise data for model training, tuning and AI applications. • Build robust systems that support schema evolution, observability, scalability and reliable production operations. Skillset • Minimum of 5 years of experience designing, building and operating large-scale data infrastructure in production environments. • Strong hands-on experience with data ingestion frameworks such as Kafka, Airbyte, Meltano or Fivetran, alongside orchestration tools such as Airflow, Dagster or Prefect. • Proven experience working with structured, semi-structured, and unstructured data, including PDFs, Excel, emails, logs, CSVs and web APIs. • Experience with columnar data stores, object storage and modern lakehouse technologies such as Iceberg, Delta or Parquet. • Strong understanding of knowledge graphs and semantic modelling, with experience using platforms such as Neo4j, RDF, Gremlin or PuppyGraph. • Experience designing and developing GraphQL and REST APIs, as well as scalable and developer-friendly data access layers. • Strong knowledge of data governance principles, including RBAC, ABAC, data contracts, lineage and data quality. • Experience with vector databases and embedding pipelines, including technologies, such as Weaviate, Qdrant or Pinecone, is highly desirable. • Familiarity with enterprise connector ecosystems, ontology versioning, graph diffing and semantic schema alignment is an advantage. • Exposure to RAG pipelines, LLM fine-tuning, Linked Data, W3C standards or modern data fabric architectures is a plus. Benefits • Salary: $150k - $200k. • Equity. Data Ops Lead New York Data Engineering Permanent Learn More Apply Now Our client, an innovative AI and Data company, is hiring a Data Operations Lead to join the team in New York. The successful candidate will own the end-to-end data lifecycle, shaping data operations, developing scalable processes, managing external partners and ensuring the highest standards of dataset quality to deliver high-quality, production-ready datasets for leading AI research teams. Responsibilities • Own the end-to-end delivery of high-quality AI training datasets, from raw audio data collection through to production-ready outputs. • Collaborate with AI research teams and external partners to define, manage and deliver dataset requirements. • Support the development and execution of commercial data agreements, ensuring successful delivery against customer expectations. • Design, build and optimize operational workflows across transcription, annotation, quality assurance and dataset production. • Develop robust data quality frameworks, acceptance criteria and validation processes to maintain delivery standards. • Lead outsourced and international data operations teams, ensuring quality, efficiency and timely delivery. • Partner with engineering teams to enhance data pipelines, tooling and operational scalability. • Identify and implement improvements to increase dataset quality, reliability and operational efficiency. • Serve as a key liaison between customers, internal stakeholders, engineering teams and external vendors. • Contribute to the company's data strategy and help scale data operations as the organization grows. Skillset • Minimum of 5 years of experience building and scaling data operations, data pipelines or AI/ML data workflows. • Proven experience working with audio, speech or multimodal datasets within AI or machine learning environments. • Strong track record managing external data partnerships, dataset delivery and AI training data programs. • Experience leading global or outsourced teams focused on data annotation, transcription and quality assurance. • Strong understanding of data quality frameworks, validation processes and operational performance metrics. • Technical proficiency across data pipelines, cloud infrastructure and AI/ML workflows. • Knowledge of digital audio fundamentals, including sample rates, voice activity detection (VAD) and audio processing formats. • Experience developing or managing AI training datasets for applications such as ASR, TTS, speaker identification or conversational AI. • Familiarity with audio processing and data tools such as Librosa, FFmpeg, SoX, torchaudio, and cloud platforms including AWS, GCP or Azure. • Entrepreneurial mindset with the ability to thrive in a fast-paced environment, take ownership and make decisions with limited information. Benefits • Salary: $180k - $220k • Comprehensive benefits package. Developer Experience and Engineering Operations Lead Dublin, Dublin Data Engineering Permanent Learn More Apply Now Our client, a global AI and data-driven organization, are hiring a Developer Experience and Engineering Operations Lead to join their growing team in Dublin, Ireland. The successful candidate will help improve how engineering teams work by making software delivery faster, more consistent and less complex across global function by removing friction across the delivery lifecycle and enabling secure, high-quality and scalable software delivery at pace. Responsibilities • Own and deliver the Developer Experience and Engineering Operations roadmap, improving how engineers build, test, release and operate software. • Identify and address friction points across the software development life-cycle using data, feedback and engineering insight. • Design and implement pragmatic engineering standards, golden paths, reusable templates and delivery playbooks. • Drive responsible adoption of AI-assisted development, including coding assistants and workflow automation. • Define clear guardrails for AI usage, including prompt patterns, quality expectations and security boundaries. • Promote adoption of internal AI platform capabilities, enabling governed reuse of AI-enabled solutions. • Improve engineering operations across onboarding, documentation, knowledge sharing and operating cadences. • Enhance CI/CD pipelines, observability, environment management, release processes and delivery transparency. • Partner with Architecture, Platform, Cloud, Security and Product Engineering teams to improve end-to-end delivery capability. Skillset • Strong background in software engineering, DevOps, SRE, platform engineering or engineering operations. • Proven track record of improving developer productivity across multiple teams or large organisations. • Deep understanding of modern SDLC practices, including cloud-native development, CI/CD, APIs, observability and automation. • Experience defining engineering standards, golden paths or reusable playbooks. • Familiarity with modern engineering toolchains such as GitHub/GitLab, CI/CD platforms, IaC, containers, developer portals and AI tools. • Strong stakeholder management and influence across engineering, architecture, security and product teams. • Experience working with globally distributed engineering teams. • Hands-on experience with AI-assisted development, including LLMs, prompt engineering and agentic workflows is a plus. • Cloud platform experience with AWS, Azure or GCP is an advantage. Benefits • Salary: €112k - €128k. • Bonus structure. • Private healthcare, life assurance and income protection. • Strong pension scheme. ** Please Note** Candidates must already be eligible to work in the Republic of Ireland. Senior Software Engineer - Data Platform New York Data Engineering Permanent Learn More Apply Now Our client, a growing HealthTech organization, is hiring a Senior Software Engineer to join their team in New York. The successful candidate will be responsible for both the technical strategy and hands-on delivery, as well as defining the architecture and building scalable, reliable data systems that enable innovation in healthcare revenue cycle management. Responsibilities • Lead the Integrations Team to design, build, and scale EHR and healthcare system integrations. • Define and deliver a clear long-term technical vision with strong execution. • Own architecture for scalable, reliable integration systems. • Establish engineering standards and best practices across the team. • Design and maintain data pipelines, ETLs and ingestion systems. • Build and evolve high-performance, reliable data infrastructure. • Develop data models aligned with a Medallion architecture approach. • Collaborate with internal teams and external healthcare stakeholders. • Translate complex requirements into effective technical solutions. • Hire, mentor and scale a high-performing engineering team. Skillset • At least 5 years of software engineering experience, with a focus on data engineering, data modelling and systems integrations. • Proven experience leading engineering teams and delivering complex technical projects. • Strong background working with healthcare systems and EHR data. • Hands-on expertise with Snowflake, SQL Mesh, Temporal (or similar orchestration tools), Python, AWS, relational databases and Medallion architecture. • Proven track record of building and optimising ETL processes and data pipelines. • Excellent stakeholder management and communication skills. • Comfortable working in a fast-paced startup environment. • Strong prioritisation and project management capabilities. • Ability to clearly communicate complex technical concepts to both technical and non-technical audiences. Benefits • Salary: $190k - $210k. • Equity, • Medical, dental and vision insurance. Senior AI Engineer (Agentic AI) Dublin Data Engineering Permanent Learn More Apply Now Our client, a global data and AI organisation, are hiring a Senior AI Engineer to join their growing team in Dublin. The successful candidate will design, build, and ship production AI systems within a fast-moving AI innovation team alongside data scientists, backend engineers, and delivery leads. Responsibilities • Build production-grade AI agent systems end to end, from design to deployment to ongoing support. • Design agentic workflows with checkpoints, approvals, safe retries and human-in-the-loop steps. • Architect agents that plan, use tools, manage memory and escalate when needed. • Build retrieval systems using vector search, reranking, and context assembly for grounded responses. • Create evaluation frameworks: offline test sets, regression gates, and live monitoring. • Instrument full observability across agent runs, including cost, latency, and failure tracking. • Build backend services in Python (FastAPI), working with Postgres, event-driven patterns and production APIs. • Deploy using Docker, Kubernetes and CI/CD pipelines. • Collaborate across teams to turn business problems into working AI solutions. Skillset • Minimum of 4 years in software engineering, applied ML or applied AI. • Strong Python skills with solid engineering practices (testing, code review, version control). • Experience building LLM-powered applications in production. • Hands-on experience with agentic frameworks (e.g. LangChain, LangGraph, CrewAI, LlamaIndex). • Experience with vector databases and retrieval systems (e.g. Pinecone, Weaviate, FAISS, Chroma). • Understanding of agent patterns e.g. ReAct, multi-agent systems, tool use, planning. • Cloud deployment experience on AWS, Azure or GCP. • Production mindset with experience in monitoring, governance, automated testing, operational readiness. • Familiarity with agentic coding tools (Cursor, Claude Code, GitHub Copilot). Benefits • Salary: €93k - €110k + Bonus. • Private healthcare. • Life assurance and income protection. • Strong pension. Interested? Reach out to kyle@alldus.com to find out more. This website uses cookies to improve your experience. 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Requirements

Must-haves

Data Science
Data Engineering
Machine Learning
ServiceNow - Consultant
ServiceNow Developer
ServiceNow Business Analyst
Data Science
Data Engineering
Machine Learning
ServiceNow - Consultant
ServiceNow Developer
ServiceNow Business Analyst
Founding Data Engineer
San Francisco, California
Data Engineering
Build reliable, scalable data ingestion and transformation pipelines across structured, semi-structured and unstructured data sources.
Develop and maintain connector frameworks that integrate enterprise systems, including ERPs, PLMs, CRMs, legacy platforms, email, Excel, documents and web APIs.
Design and enhance the Data Fabric layer, including knowledge graphs enriched with ontologies, metadata and relationships.
Prepare, enrich, normalize and vectorize enterprise data for AI and LLM applications, including RAG, summarization and intelligent alerting.
Establish and maintain data contracts, access layers, lineage, governance and data quality processes.
Develop secure APIs that enable services, AI agents and users to access and query enriched semantic data.
Partner with ML and LLM teams to provide high-quality enterprise data for model training, tuning and AI applications.
Build robust systems that support schema evolution, observability, scalability and reliable production operations.

Nice-to-haves

Exposure to RAG pipelines, LLM fine-tuning, Linked Data, W3C standards or modern data fabric architectures is a plus.

Application prep

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Keywords to mirror

machine learningaillmmlgraphqlrestawsgcpazureci/cdsqlpythonpostgresdockerkubernetesreact

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Benefits

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Dental

Vision