Sql állások XXIII. kerületben (ai és automatizáció)

Design, develop, test, document, and deploy high quality Salesforce solutions leveraging the latest capabilities of the Einstein 1 Platform, Lightning Web Components, and modern Apex patterns...

• Implementation and maintenance of large-scale data pipelines and the Azure/Databricks data platform
• Building robust, scalable pipelines using Azure Databricks (PySpark / SQL)
• Design and enforcement of data architecture standards including Delta Lake / Lakehouse architecture and data modeling

• Design of high quality Salesforce solutions leveraging Einstein 1 Platform, Lightning Web Components, and modern Apex patterns
• Development of high quality Salesforce solutions leveraging Einstein 1 Platform, Lightning Web Components, and modern Apex patterns

• Writing reusable, testable, and efficient code mainly in Python
• Mastering the integration of user-facing elements developed by front-end developers with server side logic
• Integration of data storage solutions
• Handling security and data protection professionally and precisely

• Lead and cultivate engineering talent
• Provide hands-on technical leadership
• Own and drive the technical vision
• Master stakeholder relationships
• Champion Agile and technical excellence

• Writing reusable, testable, and efficient code mainly in Python
• Delivering great user experience by the development of great user interfaces
• Handling security and data protection professionally and precisely
• Collaborative work to provide technical solutions and implement new software features

• Design, build, and deploy production-grade agentic AI and automation solutions that collect, validate, and quality-assure data at scale
• Engineer multi-agent orchestrations, data pipelines, MCP integrations, and API connections that power autonomous, end-to-end data workflows

• Writing reusable, testable, and efficient code mainly in Python
• Mastering the integration of user-facing elements developed by front-end developers with server side logic
• Integration of data storage solutions
• Handling security and data protection professionally and precisely

• Design and build scalable data warehousing and lakehouse solutions
• Operate scalable data infrastructure and lakehouse solutions
• Create and own robust ELT pipelines connecting multiple internal and external data sources
• Design and implement data models aligned with industry best practices

• Leading a team of 2-3 AI and cloud engineers focused on fast prototyping of AI/ML solutions in AWS cloud
• Providing mentoring and technical support to junior and medium teammates
• Acting as a trusted advisor to clients

• Delivery of complex data engineering projects across industries
• Leading small delivery teams
• Management of customer relationships alone or with a Project Manager
• Mentoring more junior engineers
• Evaluating the quality of junior engineers' work
• Contributing to hiring best-in-class data engineers

• Design and build agentic systems that plan, call tools and automate real business workflows
• Develop LLM-powered applications including RAG pipelines: chunking, embedding, retrieval strategies, evaluation and iteration

• Ownership of data engineering features, architecture, and code quality
• Design, implementation, and maintenance of Databricks-based data pipelines and workflows
• Building and optimization of ETL/ELT processes using Apache Spark on Databricks

• Resource account igénylése és összekötése namespacekkel
• Resource accountba kerülő komponensek megvalósítása RDS, APIk, S3, MSK, cognito, SES, private link endpoint és endpoint service nonprod és prod környezeteken
• RICE API kapcsolatok kialakítása

• Understanding underlying data structure of an ERP system (Microsoft Dynamics 365 Finance & Operations)
• Creating technical specification (design) documents for functional requirements
• Managing existing and creating new automations using Alteryx, Python, SSIS
• Creating reports and dashboards in Power BI

• Design and build scalable, reusable frameworks and services for data ingestion, transformation, and processing
• Develop and maintain the platform’s core capabilities such as orchestration, data quality validation, and schema management tools

• Design of single source of truth for product measurement including metric definitions, telemetry standards and governance model
• Definition of data contract for engineering teams instrumentation
• Building analytics and data models turning product telemetry into portfolio-level insight

• Co-design and implement QA workflows using statistical diagnostics, validation rules, and anomaly detection techniques
• Apply AI/machine learning tools to identify inconsistencies, missing values, or suspicious patterns in large-scale asset datasets

• Development, governance, deployment, and ongoing enhancement of liquidity analytics models and supporting infrastructure across asset classes
• Driving the analytics product lifecycle, including prioritizing initiatives, influencing roadmap decisions, and ensuring successful deployment into production

• Design and build artificial intelligence and machine learning solutions for enterprise clients
• Assess client needs and data for AI/ML solutions
• Select and customize models including Deep Learning, Generative AI, and Large Language Models