ai data engineer

Teradata

<div><h1 class="jobSectionHeader">Senior AI Data Engineer</h1>
Our Company
At Teradata, we believe that people thrive when empowered with better information. That&rsquo;s why we built the most complete cloud analytics and data platform for AI. By delivering harmonized data, trusted AI, and faster innovation, we uplift and empower our customers&mdash;and our customers&rsquo; customers&mdash;to make better, more confident decisions. The world&rsquo;s top companies across every major industry trust Teradata to improve business performance, enrich customer experiences, and fully integrate data across the enterprise.
As the recognized leader in data and analytics, Teradata is all about empowering high-impact business outcomes to unleash the potential of great companies. We focus every day on helping customers build lasting analytic capabilities and drive differentiated value through our flexible delivery of analytics at scale on an agile data foundation, now enhanced with cutting-edge AI, Generative AI, and Agentic AI capabilities.
Our mission is to build the world-class AI and data ecosystem for the industry leader in mega-scale Analytics platforms. Teradata is looking for senior engineers who can bridge the gap between data infrastructure and artificial intelligence. We need innovators who can architect robust data pipelines while building intelligent systems including autonomous AI agents, multi-agent systems, and AI-powered applications that can reason, plan, and take actions to solve complex business problems.
<h2 class="jobSectionHeader">Role Overview</h2>
The Senior AI Data Engineer will architect and build both the data infrastructure and intelligent AI systems that power enterprise analytics, with primary focus on:
<h2 class="jobSectionHeader">Data Engineering and Architecture</h2>
Design and build scalable data pipelines for batch and real-time processing using Teradata Vantage and enterprise data platforms. Develop integration solutions acquiring data from multiple sources, transforming it into analytics-ready formats optimized for AI/ML workloads. Leverage Teradata's advanced SQL, in-database analytics, and parallel processing. Create feature engineering pipelines and feature stores for ML operations. Implement data versioning and lineage tracking.
<h2 class="jobSectionHeader">Agentic AI and Multi-Agent Systems</h2>
Design autonomous AI agents that perceive, reason, plan, and execute complex tasks with minimal human intervention. Develop agentic workflows using LangChain, LangGraph, n8n, and orchestration tools. Build multi-agent systems where specialized agents collaborate through orchestration frameworks, task decomposition, and communication protocols. Create workflow automation with visual and code-based builders. Implement feedback loops, self-improvement mechanisms, and human-in-the-loop supervision. Deploy Model Context Protocol (MCP) for agent-to-agent and agent-to-system communication.
 <h2 class="jobSectionHeader">AI-Powered Data Applications</h2>
Build applications integrating LLMs, generative AI, and agentic capabilities with enterprise data systems. Design natural language interfaces for data operations, querying, and analytics. Create AI copilots that understand context, query databases, generate reports, and execute multi-step workflows. Develop tool-using agents that interact with APIs, databases, and external systems. Implement AI-driven automation for analysis, insight generation, and decision support.
 <h2 class="jobSectionHeader">LLM Integration and RAG Systems</h2>
Architect LLM solutions using foundation models and fine-tuned variants. Apply prompt engineering, few-shot learning, and chain-of-thought reasoning. Build retrieval-augmented generation (RAG) systems connecting LLMs with enterprise data using vector databases. Design hybrid search combining semantic and keyword approaches. Develop data ingestion for vector databases including chunking, embedding generation, and metadata management. Optimize inference with caching strategies.
 Data Management and Governance
Implement data management including lineage tracking, metadata management, quality monitoring, and cataloging. Build governance frameworks ensuring privacy, security, and compliance. Design data models for analytical and operational workloads. Create access controls and audit logging for AI applications.
 System Architecture and Infrastructure
Design production-grade architectures integrating data platforms with AI systems. Build event-driven architectures for data processing and AI workflows. Implement state management for conversational AI and long-running agents. Create comprehensive observability, monitoring, and logging infrastructure. Deploy using containerization and orchestration technologies.
<h2 class="jobSectionHeader">Key Responsibilities</h2>
<ul><li>Architect scalable data pipelines for batch and real-time processing supporting AI/ML workloads and low-latency inference</li>
<li>Design and implement autonomous AI agents and multi-agent systems for enterprise data operations</li>
<li>Build production-grade AI applications integrating LLMs, GenAI, and RAG systems with vector databases and enterprise data warehouses</li>
<li>Develop feature engineering pipelines and feature stores for ML model training and inference</li>
<li>Create AI copilots and assistants using advanced prompt engineering and agentic workflows for data querying, analysis, and insight generation</li>
<li>Design event-driven architectures integrating data processing with AI agent coordination</li>
<li>Implement comprehensive observability, data quality frameworks, and monitoring across data pipelines and AI systems</li>
<li>Build data governance solutions including security controls, AI safety measures, and compliance frameworks for production systems</li>
<li>Collaborate with data scientists, analysts, and product teams to define requirements and success metrics</li>
<li>Provide technical leadership on architecture decisions and stay current with emerging technologies in data engineering and AI domains</li> 
</ul>
<h2 class="jobSectionHeader">Required Qualifications</h2>
<ul><li>5+ years of data engineering experience building production data pipelines and ETL/ELT solutions</li>
<li>3+ years building and deploying production AI applications, LLMs, and GenAI systems</li>
<li>Strong proficiency in Python and SQL for data processing, analytics, and AI development</li>
<li>Hands-on experience with LLMs, GenAI, prompt engineering, RAG systems, and agentic workflows using frameworks such as LangChain, LangGraph, or n8n</li>
<li>Experience with vector databases, semantic search, and data processing technologies including parallel processing frameworks</li>
<li>Strong understanding of relational databases, data warehouses, and data modeling; knowledge of Teradata Vantage platform preferred</li>
<li>Solid software engineering fundamentals including API design, microservices, and distributed system architecture</li>
<li>Experience with cloud platforms (AWS, Azure, or GCP) for data and AI workloads</li>
<li>Understanding of data governance, security, and compliance best practices</li>
<li>Excellent problem-solving skills with ability to architect complex integrated systems</li>
<li>Strong communication and collaboration skills for cross-functional leadership</li>
<li>Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or related field (or equivalent experience)</li>
</ul><h2 class="jobSectionHeader">Preferred Qualifications</h2>
<ul><li>Experience with Teradata platform including Vantage, QueryGrid, VantageCloud, and AI/ML capabilities</li>
<li>Hands-on experience building multi-agent systems, agent orchestration frameworks, and agentic workflows using LangChain, LangGraph, n8n, or similar platforms</li>
<li>Knowledge of agent reasoning patterns, Model Context Protocol (MCP), and multi-step task execution strategies</li>
<li>Experience with advanced RAG techniques including hybrid search, query rewriting, re-ranking, and multiple vector database technologies</li>
<li>Knowledge of data lakehouse architectures, modern data platform patterns, and streaming technologies for real-time processing</li>
<li>Experience with data orchestration tools, workflow management systems, and feature store implementations for ML operations</li>
<li>Experience with containerization (Docker), orchestration (Kubernetes), and distributed systems for data and AI workloads</li>
<li>Knowledge of LLM evaluation frameworks and observability platforms for both data pipelines and AI systems</li>
<li>Experience with diverse data storage patterns including SQL, NoSQL, and graph databases for knowledge graphs</li>
<li>Knowledge of data versioning, lineage tracking, metadata management, and data quality frameworks</li>
<li>Understanding of AI ethics, responsible AI practices, and data privacy regulations</li>
<li>Contributions to open-source projects in data engineering or AI domains</li>
</ul>#LI-NM1</div>

Senior AI Data Engineer

Divya Stores

We are seeking an experienced Data Engineer with expertise in Dataiku to join our data team. As a Data Engineer, you will be responsible for designing, building, and maintaining data pipelines, data integration processes, and data infrastructure. You will collaborate closely with data scientists, analysts, and other stakeholders to ensure efficient data flow and support data-driven decision making across the organization.Requirements:&middot; Design and implement robust data pipelines that ingest, process, and store unstructured data formats at scale within Snowflake and GCP&middot; Leverage Snowflake's unstructured data capabilities (Directory Tables, Scoped URLs, Snowpark) to make &quot;dark data&quot; queryable and actionable.&middot; Build and maintain cloud-native ETL/ELT processes using Big Query, Cloud Storage, and Dataflow, ensuring seamless integration between GCP and Snowflake.&middot; Instead of just using LLMs, you will integrate AI tools (OCR, NLP entities, Document AI) into the engineering flow to transform unstructured blobs into structured insights.&middot; Tune complex SQL queries and Python-based processing jobs to handle petabyte-scale environments efficiently.Job Type: ContractPay: Up to $60.00 per hourExpected hours: 40 per weekWork Location: In person

Gen AI Data Engineer

JIG-SAW US, Inc.

Who We AreJIG-SAW operates 24/7 Operations Centers in Japan and Canada that proactively monitor systems, issue alerts, and deliver live incident response&mdash;keeping web services and IoT environments secure and running smoothly.<ul><li>Corporate site: https://jig-saw.com/en/</li><li>NEQTO.ai: https://neqto.ai/</li></ul>About the RoleWe are looking for a dual-threat AI and Data Engineer to build a game-changing, AI-driven IoT platform. You won&rsquo;t just be building chatbots; you will be building the &quot;nervous system&quot; of industrial environments.The core of this role is turning raw, often messy device signals into actionable intelligence. You will own the end-to-end pipeline: from integrating industrial protocols and normalizing high-velocity data streams to deploying RAG-based insights and predictive ML models. If you enjoy the challenge of making intelligence observable, reliable, and scalable across factories, buildings, and logistics hubs, this role is for you.Key Responsibilities<ul><li>Data Pipeline Architecture: Design and implement robust data ingestion and processing pipelines that receive, normalize, and transform raw data from diverse IoT sources into structured formats for AI consumption.</li><li>Protocol Integration: Implement and manage connectivity for industrial and wireless protocols including Modbus, BACnet, LoRaWAN, and MQTT.</li><li>AI/ML Development: Ship insight features combining LLM/RAG with classical ML for anomaly detection, forecasting, and predictive maintenance.</li><li>Data Engineering at Scale: Handle &quot;big data&quot; challenges&mdash;managing time-series datasets, stream processing, and ensuring data integrity for downstream models.</li><li>Retrieval &amp; Orchestration: Implement robust retrieval pipelines (chunking, embeddings, reranking) and agentic workflows to turn business questions into measurable outcomes.</li><li>Production Excellence: Own the loop end-to-end&mdash;training, deploying, and monitoring models in production with clear KPIs and guardrails.</li></ul>Required Skills &amp; Qualifications<ul><li>Data Engineering &amp; Processing: Expert-level experience in ETL/ELT, data normalization, and processing high-throughput streaming data.</li><li>LLMs &amp; RAG: Deep understanding of LangChain/LlamaIndex, agent frameworks, and tool orchestration for generating insights beyond simple text generation.</li><li>ML/DL Frameworks: Proven experience using TensorFlow, PyTorch, or Scikit-learn for time-series forecasting and anomaly detection.</li><li>Cloud &amp; Vector Infra: Proficiency in GCP/AWS/Azure and vector databases (e.g., pgvector, Pinecone, Milvus, or Weaviate).</li><li>Education: Bachelor&rsquo;s degree or higher in Computer Science or a closely related technical field.</li></ul>Experience Guidelines<ul><li>5+ years of professional Data Engineering/ML experience: building and operating production-grade platforms (ingestion, training, deployment, and monitoring).</li><li>5+ years of Industrial IoT (IIoT) domain experience: working with smart factories, logistics, energy management, or smart buildings.</li><li>3+ years delivering GenAI (LLM/RAG) features for real-world users (beyond hobbyist projects).</li></ul>Nice-to-Have<ul><li>MLOps: Experience with MLflow, W&amp;B, or SageMaker for experiment tracking and model versioning.</li><li>Industrial Connectivity: Hands-on experience integrating with Modbus (TCP/RTU), BACnet, LoRaWAN, and MQTT.</li><li>Advanced Analytics: Experience with Temporal Fusion Transformers (TFT), Prophet, or state-space models for time-series at scale.</li><li>Security: Knowledge of PII handling, RBAC, and secure device onboarding.</li></ul>Compensation &amp; Benefits<ul><li>Remote Work: Fully remote position.</li><li>Health &amp; Wellness: Comprehensive Health, Dental, and Vision insurance.</li><li>Time Off: Competitive PTO (Vacation, Sick Leave, and Company Holidays).</li><li>Salary: Competitive market-based salary dependent on location and experience.</li></ul>Portfolio Required: Please include links to your GitHub/GitLab, product sites, or technical case studies. Applications without a portfolio will not be considered.Job Type: Full-timePay: $100,000.00 - $150,000.00 per yearBenefits:<ul><li>Dental insurance</li><li>Health insurance</li><li>Paid time off</li><li>Vision insurance</li></ul>Application Question(s):<ul><li>[Required] This is the first-round screening question #1. If this field is left blank, you will not be considered.</li></ul>The key evaluation criterion is whether you can explain your reasoning logically. We value how you think more than what you already know. If a response is detected to have been generated using ChatGPT or an equivalent tool, it will be automatically rejected.For the function below, propose technical approaches you would use to implement it. Assume real production use: consider performance, scalability, operating cost, and LLM context-window limits.On an IoT data dashboard, display:1) Insights through the previous day (e.g., findings, early signs of failure, predictive maintenance).2) A list of alerts through the previous day (not simple threshold checks, but early failure signs and predictive maintenance).<ul><li>[Required] This is the first-round screening question #2. If this field is left blank, you will not be considered.</li></ul>The key evaluation criterion is the same as #1.For the function below, propose technical approaches you would use to implement it. Assume real production use: consider performance, scalability, operating cost, and LLM context-window limits.Enable users to obtain on-demand insights (findings, early signs of failure, predictive maintenance) by asking a chatbot, based on all data collected to date.<ul><li>[Required] This is the first-round screening question #3. If this field is left blank, you will not be considered.</li></ul>The key evaluation criterion is the same as #1.For the function below, propose technical approaches you would use to implement it. Assume real production use: consider performance, scalability, operating cost, and LLM context-window limits.From the data format sent by unknown IoT devices, auto-register/provision devices.1) Automatically detect and register the device with its metadata when multiple sensor values compressed/obfuscated by encoding them as binary/hex.2) Automatically detect and register the device with its metadata when multiple sensor values are human-readable (non-binary/non-hex) payloads.<ul><li>[Required] This is the first-round screening question #4. If this field is left blank, you will not be considered.</li></ul>The key evaluation criterion is the same as #1.From the data format sent by unknown IoT devices, auto-detect/map sensor attributes.1) Automatically detect sensor attributes (e.g., temperature), normalize them, and associate attributes with the database scheme. Assume multiple sensor values compressed/obfuscated by encoding them as binary/hex.2) Automatically detect sensor attributes (e.g., temperature), normalize them, and associate attributes with the database scheme. Assume multiple sensor values are human-readable (non-binary/non-hex) payloads.<ul><li>[Required] This is the first-round screening question #5. If this field is left blank, you will not be considered.</li></ul>The key evaluation criterion is the same as #1.For the function below, propose technical approaches you would use to implement it. Assume real production use: consider performance, scalability, operating cost, and LLM context-window limits.Settings recommendations: a feature that recommends configuration settings the user tends to prefer.<ul><li>[Required] Please describe in detail your professional experience using generative AI in Industrial IoT environments such as smart factories, smart buildings, logistics, and energy management. If this field is left blank, you will not be considered.</li><li>[Required] Please describe in detail your professional experience using machine learning in Industrial IoT environments such as smart factories, smart buildings, logistics, and energy management. If this field is left blank, you will not be considered.</li></ul>Education:<ul><li>Bachelor's (Required)</li></ul>Experience:<ul><li>professional ML (shipped, deployed, monitored): 5 years (Required)</li><li>professional GenAI (LLMs/RAG): 3 years (Required)</li><li>industrial IoT (e.g., smart factories, logistics): 5 years (Required)</li></ul>Work Location: Remote

AI / Data Engineer, AI/ML-powered IoT Platform

Tandym Group

<div>
 
 A recognized financial services organization in Stamford, Connecticut is actively seeking a new AI Data Analytics Engineer to build and maintain data foundations that power decision-making across the firm, encompassing analytics, data engineering, and automation. Experience with AI Configuration, AI Implementation and AI Infrastructure a must.
 
 
 
 
 
 About the Opportunity:
 
 
 <ul>
 <li>
 
 Schedule:
 
 Monday to Friday
 </li>
 <li>
 
 Hours:
 
 Standard business
 </li>
 <li>
 
 Setting:
 
 Hybrid (Tuesday to Thursdays onsite)
 </li>
 </ul>
 
 
 
 
 Responsibilities:
 
 
 <ul>
 <li>
 Define and standardize key metrics, data definitions, and reporting logic
 </li>
 <li>
 Identify data gaps and recommend process improvements
 </li>
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 Build and maintain data pipelines from various systems
 </li>
 <li>
 Design and maintain analytical layers and documentation
 </li>
 <li>
 Develop and operate ETL/ELT workflows with rigorous validation
 </li>
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 Configure, fine-tune, and optimize LLMs and advanced ML models.
 </li>
 <li>
 Build and manage prompt frameworks, embeddings, and vector databases.
 </li>
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 Translate business challenges into AI-powered workflows.
 </li>
 <li>
 Work with cloud platforms (Azure, AWS, or GCP) to scale AI workloads.
 </li>
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 Improve reliability, performance, and governance of AI systems.
 </li>
 </ul>
 
 
 
 
 Qualifications:
 
 
 <ul>
 <li>
 Proficiency with Data Warehouses and designing datasets for analytics
 </li>
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 Familiarity with SQL and experience with relational databases
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 Experience building and maintaining ETL/ELT workflows
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 Experience integrating data via APIs
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 Demonstrated ability to build dashboards and visualizations
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 Familiarity with crypto markets and on-chain analytics
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 Experience with metric layers, semantic models, and documentation
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 </ul>
 
 
 
 
 Desired Skills:
 
 
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 Certification in Data Analytics and/or Engineering
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 Advanced skills with SQL
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 </ul>
 
 
</div>

AI Data Analytics Engineer

Synsoft Global

Job Description: Senior AI/ML/Data Engineer (Python)Location: Indore (Onsite)Experience: 4+ YearsIndustry: IT / Software Development / Artificial IntelligenceAbout the Role:We are seeking a highly skilled and experienced Senior AI/ML/Data Engineer with strong Python expertise to join our team. In this role, you will be responsible for designing and implementing end-to-end machine learning solutions, analyzing complex datasets, and building data-driven products for our global clients. You should have a strong foundation in data science, machine learning, deep learning, and hands-on experience with Python and popular ML frameworks.Key Responsibilities:<ul><li>Design, develop, and deploy machine learning and deep learning models for real-world applications.</li><li>Develop and integrate Generative AI models, including large language models (LLMs), for use cases such as text generation, summarization, code generation, and RAG systems</li><li>Work with vector representations of data (e.g., embeddings) and integrate with vector databases for similarity search and retrieval-based systems</li><li>Build and manage data pipelines, ETL processes, and ensure data quality and integrity.</li><li>Collaborate with data scientists, analysts, and software developers to integrate ML models into production systems.</li><li>Conduct exploratory data analysis (EDA), feature engineering, and statistical modeling.</li><li>Develop and maintain APIs and services for model consumption using Python (Flask, FastAPI, etc.).</li><li>Optimize model performance and scalability for real-time and batch processing.</li><li>Research and experiment with new ML algorithms and techniques to solve business problems.</li><li>Document all processes, models, and systems clearly for team collaboration and future reference.</li><li>Mentor junior team members and contribute to best practices in AI/ML development.</li></ul>Required Skills &amp; Qualifications:- Bachelor&rsquo;s or Master&rsquo;s degree in Computer Science, Data Science, AI/ML, or related field.- Minimum 4 years of hands-on experience in machine learning, data engineering, andPython programming.- Strong proficiency in Python, Pandas, NumPy, Scikit-learn, TensorFlow/PyTorch, andML/DL frameworks.- Proficient in working with SQL and NoSQL databases, data wrangling, and data visualization tools.-Experience with vector databases (e.g., FAISS, Pinecone, Weaviate) and working with high-dimensional embeddings.- Solid understanding of supervised, unsupervised, and deep learning techniques.- Experience building and integrating AI voice agents / conversational systems, including speech-to-text (STT), text-to-speech (TTS), intent detection using platforms such as Twilio, ElevenLabs, Deepgram, or similar.- Hands-on experience with workflow automation and orchestration tools such as n8n, including designing event-driven pipelines, integrating third-party APIs, handlingretries/error flows, and building scalable automation for AI and data pipelines.- Experience working with Model Context Protocol (MCP) and integrating Claude(Anthropic) as a reasoning layer, including building secure connectors to business systems(e.g., CRM, databases, analytics tools), managing tool/function calling.- Experience deploying models in production and integrating with APIs or web applications.- Good grasp of software engineering practices: version control (Git), CI/CD, Docker.Preferred:-Hands-on experience with Generative AI tools and platforms (e.g., OpenAI, Hugging FaceTransformers, LangChain).-Knowledge of prompt engineering, model fine-tuning, and vector similarity search inGenerative AI pipelines.- Experience with cloud platforms like AWS, Azure, or GCP (SageMaker, BigQuery, etc.).- Familiarity with big data tools like Spark, Hadoop, or Kafka.- Experience in Natural Language Processing (NLP), Computer Vision, or ReinforcementLearning.Soft Skills:- Excellent communication skills in English, both verbal and written.- Ability to collaborate with cross-functional teams and engage with clients directly.- Strong analytical thinking, attention to detail, and problem-solving ability.- Team player with a proactive attitude and a passion for continuous learning.Why Join Us?- Be part of a growing AI/ML-focused team within an established IT organization.- Work on diverse and challenging projects across industries and geographies.- Opportunity to lead innovation and contribute to strategic AI/ML solutions.- Collaborative environment with a focus on learning and career development.Job Types: Full-time, PermanentPay: ₹1,000,000.00 - ₹2,000,000.00 per yearApplication Question(s):<ul><li>Are you comfortable to relocate to Indore, Madhya Pradesh?</li></ul>Work Location: In person

Senior AI/ML/Data Engineer (Python)

Clarivate

<div>We are seeking a Lead Product AI Data Engineer to design, build, and optimize end-to-end data architecture and scalable data platforms that power product analytics and AI-driven capabilities. This role is intended for highly experienced data engineers with 5+ years of experience and deep expertise in data architecture, dimensional data modeling, analytics architecture, and AI-ready data pipelines.<div><div><div><div><div><div><div><div><div>
In this role, you will operate as a senior individual contributor and technical lead, owning product-level data architecture decisions, complex data models, and pipelines while influencing technical direction through hands-on leadership and mentorship. You will collaborate closely with Product Managers, Data Scientists, Analysts, and Platform Engineers to ensure product and AI data is accurate, well-architected, scalable, and analytics-ready.
We are building the next generation of AI-powered analytics, insights, and decision-support platforms for the global Life Sciences and Healthcare (LSH) community&mdash;supporting mission-critical use cases such as drug discovery acceleration, treatment effectiveness analysis, real-world evidence generation, and patient journey insights.
This role blends data architecture ownership, deep technical execution, dimensional modeling excellence, and team-level technical leadership
About You &ndash; experience, education, skills, and accomplishments
<ul><li>Bachelor&rsquo;s degree in engineering or master&rsquo;s degree (BE, ME, B Tech, MTech, MCA, MS)</li><li>
5+ years of professional experience in data engineering, analytics engineering, or data architecture&ndash;heavy roles.</li><li>
Expert-level proficiency in SQL and relational database design.</li><li>
Strong programming experience in Python for data pipelines and automation.</li><li>
Deep hands-on experience with data architecture and dimensional data modeling, including star schemas, snowflake schemas, fact tables, and dimension tables.</li><li>
Strong understanding of slowly changing dimensions (SCDs), surrogate keys, grain definition, and hierarchical dimensions.</li><li>
Experience designing and operating ETL/ELT pipelines for production analytics and AI/ML workloads.</li><li>
Ability to influence technical outcomes through architectural leadership and collaboration.</li></ul>
It would be great if you also have:<ul><li>
Experience with cloud data warehouses such as StarRocks, Snowflake, Data Bricks, BigQuery, or Amazon Redshift.</li><li>
Familiarity with tools such as dbt, Airflow, Fivetran, and Segment.</li><li>
Experience working with event-driven or semi-structured data (JSON, logs, clickstream).</li><li>
Exposure to BI and visualization tools (Power BI, Tableau, SAP BusinessObjects).</li><li>
Familiarity with AWS, Azure, or GCP, including data governance and security best practices.</li></ul>
What will you be doing in this role?
Data Architecture &amp; Technical Leadership<ul><li>
Own and evolve product-level data architecture, ensuring scalability, reliability, and alignment with analytics and AI/ML use cases.</li><li>
Design and implement scalable, reliable data pipelines supporting product analytics, user behavior tracking, and AI/ML initiatives.</li><li>
Define and maintain enterprise-aligned dimensional data models (star and snowflake schemas).</li><li>
Design and maintain fact and dimension tables, ensuring correct grain, performance, and consistency.</li><li>
Contribute to and help enforce data architecture, modeling standards, naming conventions, and ETL/ELT best practices within product teams.</li><li>
Provide architectural guidance to ensure data solutions align with product requirements, platform constraints, and AI/ML needs.</li></ul>
Product &amp; AI Data Enablement<ul><li>
Partner with Product Managers, Data Scientists, Analysts, and Engineers to translate requirements into well-architected data models and pipelines.</li><li>
Prepare, validate, and document datasets used for analytics, experimentation, and machine learning.</li><li>
Support and evolve product event tracking architectures, ensuring alignment with dimensional models and downstream analytics.</li></ul>
Data Quality, Reliability &amp; Operations<ul><li>
Implement monitoring, testing, and alerting for data quality, pipeline health, and freshness.</li><li>
Ensure integrity of fact and dimension data through validation, reconciliation, and automated checks.</li><li>
Diagnose and resolve complex data issues affecting analytics, AI workflows, or product features.</li></ul>
Mentorship &amp; Collaboration<ul><li>
Mentor and support data engineers through architecture reviews, code reviews, and design discussions.</li><li>
Participate in cross-team data architecture, modeling, and pipeline design reviews.</li><li>
Collaborate with platform, cloud, and security teams to ensure scalable, secure, and production-ready data architectures</li><li>
Well-architected, enterprise-grade product and AI data platforms.</li><li>
Analytics- and AI-ready datasets built on strong dimensional and architectural foundations.</li><li>
Consistent application of data architecture, modeling, and data quality standards within product teams.</li><li>
Technical mentorship that raises the bar for data architecture and engineering excellence.</li></ul>
About the Team
The DIA team operates globally and collaborates closely with product owners, architects, and data experts. We thrive on innovation, diversity, and a mission-driven culture focused on enabling life-changing insights.
Team comprising front-end and back-end engineers, QA, UX designers, product managers, and DevOps specialists. The team is focused on delivering high-quality editorial and data intelligence solutions, with a strong emphasis on innovation, ownership, and cross-functional collaboration.
This is a fast-paced, innovation-driven environment where you&rsquo;ll have the opportunity to take ownership of features, contribute to system design, and collaborate with talented peers on impactful projects.
Hours of Work
<ul><li>Full-time, IST</li><li>
40 hours per week</li><li>
Hybrid working environment</li></ul></div></div></div></div></div></div></div></div></div>
At Clarivate, we are committed to providing equal employment opportunities for all qualified persons with respect to hiring, compensation, promotion, training, and other terms, conditions, and privileges of employment. We comply with applicable laws and regulations governing non-discrimination in all locations.</div>

Senior Product Development AI Engineer

UnitedHealthcare

<div>Optum is a global organization that delivers care, aided by technology to help millions of people live healthier lives. The work you do with our team will directly improve health outcomes by connecting people with the care, pharmacy benefits, data and resources they need to feel their best. Here, you will find a culture guided by inclusion, talented peers, comprehensive benefits and career development opportunities. Come make an impact on the communities we serve as you help us advance health optimization on a global scale. Join us to start Caring. Connecting. Growing together. 
We are looking for a skilled Data Analyst with strong expertise in cloud based data engineering, Databricks, Python, and AI enabled data solutions. The role involves developing scalable data platforms to support advanced analytics, machine learning, and enterprise BI initiatives 
Primary Responsibilities:<ul><li>
Design, develop, and optimize scalable data pipelines using Databricks, Apache Spark, and Python on Azure Cloud</li><li>
Architect and implement cloud-based data solutions leveraging Azure Data Lake, and Azure Data Factory</li><li>
Collaborate with data scientists and AI teams to operationalize machine learning models and integrate them into data workflows</li><li>
Build and maintain feature stores, model input pipelines, and real-time data processing frameworks</li><li>
Ensure data quality, governance, and security across all stages of the data lifecycle</li><li>
Develop reusable frameworks and automation scripts to accelerate data engineering and AI deployment</li><li>
Participate in Agile development processes, including sprint planning, code reviews, and CI/CD using GitHub or Azure DevOps</li><li>
Mentor junior engineers and contribute to knowledge sharing across teams</li><li>
Comply with the terms and conditions of the employment contract, company policies and procedures, and any and all directives (such as, but not limited to, transfer and/or re-assignment to different work locations, change in teams and/or work shifts, policies in regards to flexibility of work benefits and/or work environment, alternative work arrangements, and other decisions that may arise due to the changing business environment). The Company may adopt, vary or rescind these policies and directives in its absolute discretion and without any limitation (implied or otherwise) on its ability to do so</li></ul>
Required Qualifications:<ul><li>
3+ years of experience in data engineering, with a strong focus on cloud platforms and big data technologies</li><li>
Hands-on experience with Databricks, Apache Spark, and Delta Lake</li><li>
Experience with Azure Cloud Services including Data Factory, Blob Storage</li><li>
Experience with CI/CD tools and version control systems (e.g., Git, GitHub Actions, Azure DevOps)</li><li>
Expert-level proficiency in Python for data processing, automation, and integration</li><li>
Solid understanding of ETL/ELT, data modeling, and distributed computing</li><li>
Familiarity with AI/ML concepts, model deployment, and integration into data pipelines</li><li>
Proficiency in SQL and working with relational and NoSQL databases</li><li>
Proven excellent problem-solving, communication, and collaboration skills</li></ul> 
Preferred Qualifications:<ul><li>
Azure certifications (e.g., Azure Data Engineer Associate, Azure AI Engineer Associate)</li><li>
Experience with MLflow, Feature Store, or other MLOps tools</li><li>
Exposure Snowflake, Power BI, or other BI platforms</li><li>
Knowledge of data governance, privacy, and compliance standards</li></ul> 
At UnitedHealth Group, our mission is to help people live healthier lives and make the health system work better for everyone. We believe everyone-of every race, gender, sexuality, age, location and income-deserves the opportunity to live their healthiest life. Today, however, there are still far too many barriers to good health which are disproportionately experienced by people of color, historically marginalized groups and those with lower incomes. We are committed to mitigating our impact on the environment and enabling and delivering equitable care that addresses health disparities and improves health outcomes - an enterprise priority reflected in our mission.</div>

Data Analyst - Cloud, Databricks and AI

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