• Prepare technical architecture and design documents; • Develop data modeling; • Manage the Databricks environment; • Manage AWS, Azure and GCP cloud components; • Develop solutions using (Java / Python / Spark / Scala); • Configure network components in hybrid environments; • Define best practices for relational and non-relational data storage; • Implement improvements in Data Lake and Data Warehouse environments; • Implement and manage SQL and NoSQL databases; • Define and develop DevOps, Docker and Kubernetes solutions; • Discuss and define the best solutions for system integrations; • Support the creation of data governance processes; • Advise on the best solutions to support the company's self-service BI strategy.

Role Description FableticsOS is looking for a Senior Data Analytics Engineer to strengthen the data foundations of our Global Supply Chain and Merchandise Planning domains. You will model and build the data that powers how we move product around the world and decide what to make and buy — connecting logistics, sourcing, ERP, and planning systems into trusted, reusable data products. As a Senior Data Analytics Engineer, you will design and build scalable, high-quality data models and pipelines that support our supply chain and merchandise planning organizations. You'll lead technical design, establish engineering standards, and deliver analytical data products end-to-end—from discovery and architecture through implementation. Partnering closely with planning, sourcing, logistics, and commercial teams, you'll translate business needs into data solutions that improve planning, forecasting, and operational decision-making. This role focuses on two primary domains: - Logistics & Supply Chain Visibility: Integrate ETA, transit, freight, and landed-cost data from logistics platforms into Snowflake, combining it with PLM, ERP, and planning data to improve purchase order planning and visibility. - Planning Platform Data Architecture: Own the data feeds and integrations that power our supply chain and merchandise planning platform, ensuring data integrity while supporting expansion into assortment, production, and capacity planning. Supporting both areas is a strong foundation in our enterprise data warehouse (EDW), including core models, standards, and architectural principles that underpin all analytics and data engineering work across the organization. Qualifications - 6+ years of experience in a data or software-adjacent field, working with systems and data infrastructure at scale. - Experience designing and implementing mature data pipelines across a data lake and Enterprise Data Warehouse (EDW), using Dimensional (Kimball) modeling methodologies. - Experience integrating data across fragmented or disparate source systems into coherent, reliable data products. - Experience with ELT/ETL tooling and workflow orchestration (e.g., Apache Airflow). - Strong experience with relational databases and SQL in an analytical context. - Experience with data exploration, visualization, and data storytelling. - Excellent written and spoken communication, with a proven ability to work with a wide range of both technical and non-technical stakeholders. - Highly skilled at writing and maintaining high-quality, reusable code, applying design patterns and meeting coding standards. - A pragmatic adopter of AI in your own work — you use LLMs and AI-assisted tooling to accelerate development, code review, documentation, and data exploration, with good judgment about where they help and where they don't. - Track record of independently owning complex, ambiguous data initiatives end-to-end — making the architectural decisions, setting standards, and influencing technical direction across teams. - Experience with Snowflake, Airflow and Tableau is preferred. Requirements - Integrate logistics and planning data: Bring logistics visibility data (ETAs, transit times) into Snowflake and connect it with PLM, ERP, and planning sources for accurate PO loading and freight/landed-cost estimates. - Build trusted, reusable data products: Connect disparate supply chain and merchandising datasets into curated data products across our data lake and EDW, with reliable, well-defined metrics that power self-serve analytics across teams. - Own data quality and integration integrity: Take ownership of data quality across fragmented upstream systems — building the monitoring and controls that keep critical feeds reliable and integrations healthy as the planning landscape evolves. - Model data using best practices: Apply established Data Warehousing methodologies, primarily Dimensional (Kimball) modeling. - Turn complexity into insight: Transform large, complex datasets into actionable insights, delivered in a consumable format for historical and predictive analysis. - Perform data governance: Handle technical stewardship, data classification, compliance, and security considerations. - Validate before release: Build data visualizations to trial and validate data products before releasing them to customers. Benefits - Hybrid Work Schedule* - Discretionary Paid Time Off* - Summer Fridays* - Healthcare Plans - Employee Discounts - 401k - Annual Bonus Program - Equity Program* - And More *Varied for retail, fulfillment and fully remote roles. The annual base salary range for this position is from $120,000-$140,000. The range provided includes the base salary that FableticsOS expects to pay for the role. Offered base salary will be dependent on factors including the scope and complexity of the role, candidate’s related work experience, subject matter expertise and work location.

Role Description The Data Analytics Engineer 3 is responsible for designing and creating tables within data marts, data lakes, and data warehouses, along with all related extraction, transformation, and load of data functions. The role involves: - Building systems that collect, manage, and convert raw data into usable information for analytics developers and business analysts. - Making data accessible for organizations to evaluate and optimize performance. - Developing and testing architectures for data extraction and transformation for predictive or prescriptive modeling. - Expanding and optimizing data and data pipeline architecture. - Optimizing data flow and collection for cross-functional teams. - Taking direction and guidance from lead data engineers and department leadership. - Mentoring junior data engineers and promoting data education across the organization. Qualifications - Required: Bachelor's Degree in Business, Computer Science, Engineering, Information Systems, Public Health, or related field. - Preferred: Master's Degree in Computer Science, Business, Healthcare Management, Information Systems, or related field. - 5 years of experience building data pipelines using ETL technologies such as SSIS or Informatica. - 3 years of SQL development with SQL Server/Oracle environment (Required). - 2 years of experience with cloud data platforms and associated tools such as MS Azure, AWS, or GCP (Required). Requirements - Deep understanding of modern data architectures, including lakehouse design patterns and scalable data processing approaches. - Hands-on experience with Azure data platforms, including ADLS, Azure Data Factory (ADF), Databricks, and related lakehouse technologies. - Strong experience designing and implementing metadata-driven, scalable data pipelines. - Proficiency in Python and PySpark for building data pipelines, transformations, and distributed processing workloads. - Experience integrating data across systems using APIs and other modern data connectivity patterns. - Proficiency with DevOps and DataOps practices, including CI/CD for data pipelines and cloud infrastructure. - Advanced SQL skills with experience managing and optimizing relational data platforms. - Familiarity with Microsoft Fabric is a plus. - Experience with healthcare data platforms such as Epic Cogito is a plus. Benefits - Comprehensive benefit offerings for eligible employees. - Overview available on the benefit section of our career site: jobs.franciscanhealth.org.

Role Description In your new role as an Analytics Engineer at i6 you will be responsible for designing, building, and maintaining complex data models using dbt (Jinja, Macros, Incremental strategies) and managing high-availability ingestion pipelines. You will focus on the "build" phase of the data lifecycle—implementing DataOps best practices, including CI/CD via GitHub Actions and automated testing frameworks. You will optimise warehouse performance (BigQuery/Snowflake) to support millions of rows of data and collaborate across teams to ensure data contracts are met and data quality is guaranteed before it reaches the end-user. What you will do - Own the Transformation Layer: Design, build, and maintain complex dbt models to power internal BI and external customer analytics. - Pipeline Management: Manage and monitor data ingestion pipelines to ensure high availability and low latency. - Performance Tuning: Optimise cloud data warehouse costs and query performance (clustering, partitioning) for sub-second response times. - Data Quality & Testing: Build and maintain automated testing frameworks (dbt test, Great Expectations) to proactively catch data issues. - DataOps: Maintain CI/CD pipelines (GitHub Actions) for data deployment, applying software engineering principles to data workflows. - Collaboration: Partner with Data Analysts to provide clean models and work with the Data Engineering Lead on architectural infrastructure decisions. - Technical Documentation: Document data models, macros, and transformation logic clearly to ensure team scalability. Qualifications - 3-4+ years of experience in Data or Analytics Engineering. - Expert SQL: Ability to write complex window functions, optimise joins, and debug query plans. - dbt Expertise: Deep hands-on experience in production (snapshots, incremental models, custom generic tests, Jinja/Macros). - Cloud Data Warehousing: Deep understanding of BigQuery or Snowflake, specifically clustering and partitioning strategies. - Version Control: Expert-level comfort with Git, branching strategies, and Pull Request workflows. - Orchestration: Experience with Airflow, Dagster, or similar tools. - NoSQL Knowledge: Understanding of NoSQL structures and how to transform them into relational models. - Data Contracts: Understanding of data contracts and their role in pipeline stability. Requirements - Experience with Google Cloud Platform (GCP) and its data services (BigQuery). - Familiarity with Infrastructure-as-Code (Terraform). - Experience with Containerization (Docker). - Programming proficiency in Python for automation and data manipulation. Benefits - 25 days annual leave + your birthday day off. - Private healthcare. - 5% pension contribution.