Role Description We are seeking an experienced Data Platform Architect to design and implement scalable, high-performance data platforms that support enterprise analytics, machine learning, and reporting initiatives. This role is critical in building the data foundation that enables data-driven decision-making across the organization. The ideal candidate will bring deep expertise in modern data architectures, including lakehouse models and cloud-based ecosystems, along with the ability to align technical solutions with business objectives. Responsibilities - Design and implement enterprise-grade data platforms for large-scale analytics and AI use cases - Develop and optimize data pipelines for ingestion, transformation, and storage (ETL/ELT) - Evaluate and recommend data technologies based on business and technical requirements - Define and implement data governance, security, and compliance frameworks - Collaborate with cross-functional engineering teams to integrate data systems seamlessly - Optimize data infrastructure for performance, scalability, and reliability - Enable advanced analytics and machine learning workloads - Provide technical leadership, guidance, and mentorship to data engineering teams Qualifications - Extensive experience designing and implementing modern data architectures - Strong knowledge of cloud data platforms (AWS, Azure, or GCP) - Hands-on experience with ETL/ELT tools and data pipeline development - Solid understanding of data governance, security, and compliance practices - Experience working in large-scale enterprise data environments - Strong expertise in database technologies and data modeling - Ability to translate complex business requirements into scalable technical solutions Must-Have Skills - Proven experience designing enterprise-scale data platforms supporting AI/ML workloads - Experience integrating legacy systems with modern cloud data platforms - Strong problem-solving, architecture, and decision-making skills - Excellent communication and collaboration abilities Preferred Qualifications - Experience with lakehouse architectures (e.g., Delta Lake, Iceberg, or similar) - Familiarity with big data processing frameworks (e.g., Spark) - Knowledge of data observability and monitoring tools - Experience with MLOps and data platform optimization for AI use cases Work Environment - Remote / Hybrid flexibility - Collaborative, fast-paced, and innovation-driven environment For more details reach at resumes@navitassols.com

Role Description ADTRAV is seeking a Principal Data Engineer to be responsible for leading the design, development and implementation of components within ADTRAV’s business intelligence application. This position has access to data as part of a government contract, and it is a requirement of the contract for individuals to be a U.S. citizen. Candidates must be able to show proof upon hire that they meet this requirement. In addition, this position is required to pass a government clearance process. - Lead the design, development, and implementation of major components of the RezIntel platform. - Work with cross-functional teams to gather requirements and understand data needs. - Implement data ingestion, storage, and processing solutions for structured and unstructured data. - Ensure the data storage system is scalable and highly available. - Develop and maintain data pipelines and ETL/ELT processes. - Maintain a strong understanding of a data lifecycle. - Conduct data modeling and create documentation like data definitions, data flow diagrams, and process definitions. - Automate and ensure data quality. - Design and develop database objects, tables, stored procedures, views, and ETL pipelines. - Conduct performance tuning of T-SQL, including stored procedures, views, functions, etc. - Mentor and instruct other engineers on best practices. - Analyze, solve, and correct issues in real-time. - Provide engineering estimates. - Use Agile development tools to track and communicate project progress. - Stay up to date on new information technologies and emerging trends, and their possible application in the company. - Work non-standard business hours to attend meetings and provide support via text/phone/email in a 24/7/365 environment. - Represent ADTRAV by embracing the company values and maintaining effective working relationships with employees, partners, vendors, and clients. - Adhere to company policies and procedures. - Other duties as assigned. Qualifications - Bachelor’s Degree in related field or equivalent experience required. - 10+ years of experience in ETL technologies and concepts like SSIS and Python/Pandas. - 8+ years of experience in Data Warehousing, DataVault, and Data Mart concepts and implementation especially in relational databases like SQL Server. - Ability to mentor engineering teams on best practices. - Strong SQL knowledge and work experience with relational databases such as Microsoft SQL Server, or PostgreSQL. - Experience in architecting, crafting, and developing highly scalable distributed data processing systems. - Experience architecting DataVault and/or Kimbell systems. - Experience in using visualization and reporting tools such as SSRS, Qlik Sense, and PowerBI. - Experience implementing data solutions in Azure and on-prem data centers using ETL tools, queues, batch scheduling services and various storage solutions. - Background in data analysis and data modeling. - Experience in Agile SDLC methodologies (Kanban and Scrum). - Experience building and deploying applications using continuous integration pipelines and automated deployment tools such as Azure DevOps, etc. - Hands on experience with Python, Pandas, and other Python ETL tools required. - Ability to estimate the work required and deliver the project on schedule as estimated. - Experience with source control such as Git or TFS. - Experience with issue tracking systems such as Jira or Azure DevOps. - Ability to learn ADTRAV’s business and products quickly and adapt to a dynamic development environment. - Excellent English written and verbal communication skills. - Ability to communicate ideas and concepts clearly and concisely, while also being open to receive feedback and direction as needed. - Highly self-motivated and able to prioritize and manage multiple tasks with varying deadlines. - Able to work in a fast-paced environment while balancing both strategic and tactical responsibilities. - Detail oriented with strong organizational and analytical skills. - “Can do” problem solving mentality, while still respecting standards, quality, and security. - Able to present a professional and positive demeanor with internal and external customers/clients and work cooperatively. - Ability to work remotely and meet the company home-office requirements, including stable, high-speed internet. - Able to maintain confidentiality of company and client information. - Able to work outside of normal business hours and provide 24/7/365 support to team. - Able to successfully pass a credit, criminal, and/or employment reference background check. - Able to successfully pass a government clearance process as required by clients. Benefits - Competitive salary range: $140,000-160,000 a year, with consideration given outside of this range based on experience. - Medical, dental, vision, life, and disability insurance. - Flexible Spending Accounts. - 401(k) plan. - Paid Time Off (PTO).

Role Description We are looking for a Data Quality Engineer with strong experience in Azure and Databricks to ensure data quality, reliability, and consistency across modern data platforms. This role focuses on validating data pipelines, implementing automated quality checks, and collaborating closely with Data Engineering and business teams to guarantee accurate and production-ready data assets. - Design and implement a data quality framework across Bronze, Silver, and Gold layers — defining validation rules, threshold tolerances, and alerting standards. - Build and maintain automated data quality checks within Databricks pipelines — row counts, null checks, referential integrity, schema validation, and business rule assertions. - Own reconciliation between source systems and Databricks layers — ensuring source data lands accurately and transformations produce expected outputs. - Validate identity resolution outputs in the Silver layer — reviewing match rates, investigating false positives and false negatives, and ensuring enterprise identifiers are being assigned correctly across source populations. - Perform end-to-end pipeline testing — validating that data flows correctly from ingestion through to the Gold layer and that downstream reporting outputs reflect accurate data. - Partner with Data Engineers to define acceptance criteria for each sprint’s pipeline and data model deliverables before they are promoted to production. - Support UAT with client business stakeholders — helping them validate that Gold layer outputs meet their reporting requirements. - Document all QA processes, test results, and data quality findings in a format that can be handed off to the client team at engagement close. - Monitor pipeline health post-deployment — investigating and triaging data quality incidents and working with engineers to resolve root causes quickly. Qualifications - Experience working with Azure-based data platforms, including Databricks. - Strong understanding of data quality frameworks and testing methodologies for data pipelines. - Experience validating ETL/ELT processes and working with layered architectures (Bronze, Silver, Gold). - Strong SQL skills and experience analyzing large datasets. - Experience implementing automated data validation and reconciliation processes. - Familiarity with data pipeline monitoring, alerting, and troubleshooting. - Ability to collaborate with Data Engineers and business stakeholders. - Strong analytical thinking and attention to detail. - Experience documenting QA processes and results in a structured manner. - English: Advanced (required for effective communication with global teams). Requirements - 3+ years of experience in Data Engineering or Data Quality roles. Benefits - Learning Opportunities: - Certifications in AWS (we are AWS Partners), Databricks, and Snowflake. - Access to AI learning paths to stay up to date with the latest technologies. - Study plans, courses, and additional certifications tailored to your role. - Access to Udemy Business, offering thousands of courses to boost your technical and soft skills. - English lessons to support your professional communication. - Travel opportunities to attend industry conferences and meet clients. - Mentoring and Development: - Career development plans and mentorship programs to help shape your path. - Celebrations & Support: - Special day rewards to celebrate birthdays, work anniversaries, and other personal milestones. - Company-provided equipment. - Flexible working options to help you strike the right balance. - Other benefits may vary according to your location in LATAM. For detailed information regarding the benefits applicable to your specific location, please consult with one of our recruiters.

• Own the end-to-end data architecture for the Data Warehouse Foundation, designing for AI-first consumption across GPT assistants, AI agents, predictive models, and operational intelligence — in addition to BI and reporting. • Lead data modeling across all 11 departments, designing canonical enterprise data models that serve cross-functional AI and analytics use cases without duplication or fragmentation. • Design and implement the contextual intelligence layer — including RAG architecture, vector store strategy, knowledge base ingestion pipelines, and document and unstructured data processing — that powers Agiloft's enterprise knowledge system. • Build and maintain the agentic data integration layer: real-time and near-real-time data access patterns, agent memory and state persistence design, orchestration data requirements, and agent output integration back into the warehouse. • Own the AI/ML feature layer — feature engineering strategy and standards, training data pipeline design, feature store architecture, and model output integration — enabling predictive analytics across churn, pipeline health, and operational forecasting. • Design and govern the operational data and GPT context layer, including structured context feed design for GPT assistants, data freshness and access SLAs for AI use cases, and cross-departmental data reuse standards. • Lead the Data Warehouse Foundation build in partnership with the external consulting team — setting architecture standards, reviewing implementation against AI-first principles, and ensuring the five-wave build plan delivers a foundation that serves the full intelligence architecture. • Design and manage data ingestion, ELT/ETL, and orchestration pipelines across all source systems, ensuring reliability, performance, and cost efficiency. • Establish and enforce AI data engineering standards across the organization — prompt-adjacent data design, agent data access patterns, reusable pipeline components, and quality assurance processes. • Own data access policy design and least-privilege access controls in partnership with Security, ensuring data made available to AI systems is governed, auditable, and compliant. • Define data quality standards and monitoring processes for AI-consumed data, where quality failures have direct impact on model and agent performance. • Partner with the Principal Data and Integrations Architect on infrastructure design, ensuring data modeling and AI consumption requirements are incorporated into pipeline and architecture decisions from the start — not retrofitted after build. • Partner with the VP FP&A and Manager of BI & Data to ensure the semantic and metrics layers are technically sound and serve both AI use cases and reporting requirements. • Manage the AI Ops data architecture roadmap, translating business and AI use case requirements from all 11 departments into sequenced, prioritized technical work. • Maintain documentation and knowledge transfer standards for all data architecture, pipelines, and integration patterns — ensuring AI Ops-built infrastructure is reusable, auditable, and not dependent on any single individual. • Collaborate with the AI Agent Engineer and GPT & AI Systems Lead to ensure data infrastructure supports agent orchestration, retrieval-augmented generation, and multi-step reasoning workflows. • Define the roadmap for data science and AI data work in partnership with the VP of AI Operations — this role does not take direction from IT on resource allocation or prioritization. All roadmapping is managed within AI Operations. • Evaluate and recommend data tooling, frameworks, and platform components in alignment with AI Ops' technology-agnostic, build-for-leverage approach. • Other duties as assigned.