Play a critical role in shaping our data strategy and developing data products that support our mission of expanding access to mental healthcare by leading the charge in using data to improve patient outcomes, streamline operational workflows, and support business growth.
The Data Analytics Consultant will be a pivotal member of the Analytics Team, responsible for delivering critical insights that drive data-driven decision-making for financial institution clients. This role will be part of the data engineering team and will be responsible for ensuring that client-provided data is reviewed, imported, transformed, and quality-controlled into specifically requested data warehouses for ongoing projects and services.
Trility Consulting is looking for an Azure Data Consultant to lead Proof of Concept (PoC) initiatives by implementing Microsoft Fabric. The candidate will collaborate with clients to test Fabric’s performance compared to on-premise systems and build robust data solutions, applying expertise in Fabric, ETL development, and cloud analytics platforms to test, build, and optimize solutions.
Lead a team of data professionals dedicated to supporting Revenue, Marketing, and Finance teams. You will oversee the business intelligence system, data analysis, and data strategy, playing a critical role in driving data-informed decision-making and fostering a culture of analytics excellence. Balance strategic vision with operational execution to empower stakeholders across the organization to achieve their goals through actionable insights!
Looking to build the next generation of data pipelines and applications across the development of innovative new systems and solutions using a rapidly changing landscape of emerging technologies, including generative AI and large language models. Primary focus will be building reliable, scalable, and efficient pipelines for use in LLMs and crafting our vision for LLM analytics. Responsible for designing, building, and scaling data pipelines across a variety of source systems.
Evolve our foundational data infrastructure to primarily support our finance department. You will own the analytics infrastructure for these products end-to-end -- from data ingestion, to reporting, to activation -- ensuring high data quality and availability for our data sets. Work with the business, engineering, and analytics teams to lay the foundation for effective reporting and analysis needed to scale up these ventures.
Support the design and development of data infrastructure and pipelines to enable analytics, reporting, and operational data needs. Work closely with senior engineers and cross-functional teams to build reliable and efficient data workflows. This role is ideal for someone with a solid foundation in SQL and data engineering concepts, looking to deepen their expertise in a cloud-first.
Design and implement scalable, well-documented data pipelines and semantic models using Microsoft Fabric and Power BI to support enterprise-wide analytics. Translate complex business requirements into performant data models, calculated measures, and interactive dashboards using DAX and Power Query. Integrate and transform data from Dynamics 365 and other enterprise sources into analytics-ready formats for self-service BI and decision support.
Bring your passion and feel the energy in this Remote/Hybrid role across Australia. As an Informatica’s Intelligent Data Management Cloud (IDMC) Developer, you will specialize in leveraging the IDMC platform to ensure data quality, accessibility, and governance across the organization. This role is pivotal in designing, implementing, and maintaining data cataloging, data quality, data integration, and metadata management solutions using Informatica's suite of tools, focusing on Catalog, Data Quality & Reference360.
As an Analytics Engineer at Imagine Pediatrics, the role is responsible for developing and maintaining the core data models that power analytics and reporting across the organization. It requires a mix of technical expertise and analytical thinking, applying engineering best practices to transform raw data into well-defined, consumable datasets.