Company Description Blend is a premier AI services provider, committed to co-creating meaningful impact for its clients through the power of data science, AI, technology, and people. With a mission to fuel bold visions, Blend tackles significant challenges by seamlessly aligning human expertise with artificial intelligence. The company is dedicated to unlocking value and fostering innovation for its clients by harnessing world-class people and data-driven strategy. We believe that the power of people and AI can have a meaningful impact on your world, creating more fulfilling work and projects for our people and clients. For more information, visit www.blend360.com. What is this position about? We are looking for Data Architect to lead the initial phase of a data platform transformation. This person will be responsible for evaluating the current Databricks environment, defining the target architecture, and establishing the technical foundation for scalable, governed data solutions in the cloud. Job Description Job Description · Assess the existing Databricks environment and prior implementations to support architectural decision-making · Analyze existing components and document recommendations around reuse, refactoring, or redesign · Support source system analysis by documenting data structures, dependencies, and ingestion considerations · Translate architectural direction into clear technical guidance for delivery teams · Own the creation and maintenance of architecture and delivery documentation, including: o Medallion architecture definitions (Bronze, Silver, Gold) o Data flow and ingestion pattern documentation o Data modeling and analytics-readiness standards o Naming conventions and documentation templates · Document architectural decisions, assumptions, trade-offs, and constraints · Maintain runbooks, reference materials, and architectural artifacts used by engineering teams · Ensure documentation stays current as implementations evolve · Support definition of logical and analytical data models, particularly for Silver and Gold layers · Review and validate data modeling approaches for alignment with architectural standards · Assist with SQL-based transformation logic design and validation · Perform lightweight analysis or prototyping (SQL or PySpark) to help validate architectural patterns · Support governance and security standards leveraging Unity Catalog through documentation and guidance · Assist in documenting: o Data ownership and domain concepts o Access and environment standards o Metadata, lineage, and usage expectations · Partner with Platform and DevOps teams to document agreed-upon governance and security patterns · Act as a liaison between Architecture, Data Engineering, Analytics, and Platform teams · Clarify architectural intent and standards for delivery teams · Gather feedback from engineering teams to improve clarity and usability of documentation · Reduce rework and ambiguity by ensuring expectations are clearly documented upfront Qualifications Qualifications ● Bachelor’s degree in Computer Science, Information Systems, or a related field ● Strong working knowledge of Databricks and modern lakehouse architectures ● Advanced SQL skills for data analysis, modeling, and transformation design ● Working knowledge of Python (PySpark preferred) for analysis and prototyping ● Experience supporting ETL/ELT pipelines in cloud environments (Azure preferred) ● Familiarity with medallion architecture concepts (Bronze/Silver/Gold) ● Experience supporting data governance frameworks (Unity Catalog or similar) ● Proven ability to produce clear, structured technical documentation Nice to Have ● Experience with Delta Lake, Delta Live Tables, or Auto Loader ● Familiarity with data quality and validation frameworks ● Exposure to healthcare or other regulated data domains ● Basic understanding of CI/CD and platform concepts (documentation-level) ● Awareness of data privacy and compliance requirements (HIPAA or similar) What about languages? Excellent written and verbal English for clear and effective communication is a must! How much experience must I have? We're looking for someone with 5+ years of experience, with demonstrated strength in data platform analysis, documentation, and architectural support Additional Information Our 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. 👩‍🏫 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. So what are the next steps? Our team is eager to learn about you! Send us your resume or LinkedIn profile below and we’ll explore working together!

• Lead the design, development, and scaling of complex data pipelines and platforms • Partner with business leaders to translate requirements into production-ready data solutions • Own end-to-end data solutions including modeling, performance, reliability, and cost optimization • Implement CI/CD, testing, monitoring, and data quality best practices • Provide technical mentorship to junior team members

• Design, build, and maintain backend services and REST APIs that serve data from various SQL subsystems and other data sources • Develop well-tested, production-grade Python services with clean API contracts, proper authentication, versioning, and error handling • Work closely with the Analytics Engineering team to expose modeled data (billing, settlement, finance) through APIs that downstream consumers can rely on • Build internal tooling and services that enable the broader organization to self-serve their data needs without writing SQL • Participate in code reviews, system design discussions, and engineering best practices across the Infrastructure org • Contribute to service observability: logging, metrics, alerting, and on-call practices for the services you own • Maintain and improve existing data pipelines that move data from source systems into Snowflake (Airflow, Airbyte) • Contribute to the dbt project alongside the Analytics Engineering team — model improvements, test coverage, and data quality • Support data governance practices including access controls, lineage documentation, and data quality standards

AgileEngine is an Inc. 5000 company that creates award-winning software for Fortune 500 brands and trailblazing startups across 17+ industries. We rank among the leaders in areas like application development and AI/ML, and our people-first culture has earned us multiple Best Place to Work awards. WHY JOIN US If you're looking for a place to grow, make an impact, and work with people who care, we'd love to meet you! ABOUT THE ROLE As a Data Engineer, you’ll be part of a small, senior team building a cloud-native data platform on AWS from the ground up — moving raw data through Bronze, Silver, and Gold layers into clean, trusted, analytics-ready datasets that directly power business decisions. Working closely with the Data Architect and Head of Data, you’ll have real visibility into the full data lifecycle: pipeline design, data quality, governance, and delivery. If you want a role where your engineering work is foundational rather than incremental, and where the stack — S3, Glue, Redshift, dbt, Airflow, PySpark — is genuinely modern, this is it. WHAT YOU WILL DO - Build and maintain scalable ETL and ELT pipelines across AWS services; - Implement medallion architecture for data ingestion, transformation, and delivery; - Collaborate with data architects on data modeling and schema design; - Develop ingestion frameworks for structured, semi-structured, and streaming data; - Integrate data quality, lineage, and observability into pipelines; - Work with analytics and business teams to deliver consistent and well-documented data; - Write clean and testable Python and SQL code following best practices; - Support data governance and security standards including compliance requirements; - Monitor pipeline performance, troubleshoot issues, and optimize scalability and cost efficiency. MUST HAVES - 5+ years of experience as a Data Engineer working with AWS data ecosystems; - Strong experience with AWS services (S3, Glue, Lambda, Kinesis, Redshift, Athena, Step Functions); - Proficiency in Python, PySpark, and SQL for data transformation; - Strong understanding of ETL design patterns, batch and streaming data processing; - Knowledge of data modeling (star schema, snowflake schema, incremental processing); - Experience with orchestration tools (Airflow, Step Functions, dbt); - Understanding of data governance, data quality frameworks, and CI/CD for pipelines; - Experience working in agile, cross-functional environments; - Upper-intermediate English level. PERKS AND BENEFITS - Professional growth: Mentorship, TechTalks, and personalized growth roadmaps. - Competitive compensation: USD-based pay with education, fitness, and team activity budgets. - Exciting projects: Modern solutions with Fortune 500 and top product companies. - Flextime: Flexible schedule with remote and office options.