Role Description We're looking for a Data Engineer II to join our Data Engineering team, which builds and governs the data foundation that powers the business. You'll work within our stack — Python ingestion pipelines, Airflow orchestration, and Snowflake/Databricks — helping move data reliably and securely from source to decision-ready output. This is an entry-level role. - Implement ingestion pipelines and Airflow DAGs from a senior engineer's design, using the team's scaffolding and conventions — including writing the code, unit tests, and documentation. - Support data security and governance work, such as PII masking and access controls, following established patterns. - Contribute to data delivery work, including reverse ETL integrations, under guidance from senior engineers. - Add and extend fields in existing pipelines, incorporating review feedback and applying learned patterns on future work. - Take oncall pages for pipeline failures, work through runbooks, and escalate with clear context when needed. - Pair with senior engineers on data integrity issues you can't yet diagnose alone. - Write clear, reviewer-friendly PR descriptions and ask clarifying questions before starting new work. - Flag blockers early and with context rather than going quiet when stuck. - Build strong working relationships with internal stakeholders (BI analysts, other data engineers, data scientists) and help gather and clarify requirements. - Conduct and participate in code and system inspections. - Help the team define and adhere to data engineering best practices. - Mentor more junior data engineers as you grow into the role. Qualifications - 1–3 years of professional software or data engineering experience. - A self-learner with a strong ability to gather, evaluate, and analyze requirements. - Solid foundation in Python and deep understanding of SQL and ETL/ELT for complex data transformations. - Comfort reading and writing unit tests, and working within an established codebase and conventions. - Familiarity with (or eagerness to quickly learn) workflow orchestration tools like Airflow (Managed Workflows for Apache Airflow). - Basic understanding of data pipeline concepts: ingestion, idempotency, scheduling, and data quality. - Knowledge of several of the following technologies: Snowflake, Databricks, AWS, dbt, Tableau, MongoDB, PostgreSQL. - Familiarity with Git-based version control and PR-based code review workflows. - Strong communication skills — asks clarifying questions, writes clear PR descriptions, and escalates blockers with useful context rather than staying stuck silently. - A growth mindset: takes review feedback well, improves processes, and champions best practices to avoid technical debt. Requirements - Exposure to cloud data warehouses/lakehouses (Snowflake, Databricks, AWS) and data catalog/lineage tooling. - Familiarity with dbt, Tableau, MongoDB, or PostgreSQL. - Familiarity with reverse ETL tools or patterns (e.g., Segment, LaunchDarkly, Kafka, S3-based delivery). - Exposure to PII masking, data security, or RBAC/access governance concepts. - Exposure to observability/monitoring tooling (e.g., Datadog) for pipeline health and alerting. - Background in gaming, betting, e-commerce, or another regulated/high-compliance industry. - Familiarity with responsible handling of customer/PII-sensitive data. Benefits - Join a team that's literally described as "the foundation" — everything at FBG, FES, and FMX runs on the data we ingest, govern, and deliver. - Learn from senior and staff data engineers on a well-invested, modern data platform, with a clear growth path from DE2 into independent ownership. - Work on high-visibility, high-trust systems: regulatory and financial reporting, PII security, and data governance that the business depends on. - A culture built around clear tenets: standardize before you scale, own the outcome (not just the ticket), and clarity over complexity. - Collaborative culture with strong engineering practices, code review, and mentorship. Salary Range $118,000 — $156,000 USD

• Focus on engineering, building, and maintaining data pipelines, applications, workflows, and governance capabilities inside the Palantir platform, particularly Code Repositories, Workshop, Slate, and AIP. • Build and configure data pipelines using Code Repositories and Pipeline Builder. • Build applications and workflows using Workshop and Slate within Palantir. • Develop data pipelines involving collecting, transforming, cleaning, and validating data, using Palantir. • Develop and maintain governance applications that monitor platform performance, compliance, and operational health. • Implement and maintain platform‑driven automations and monitoring capabilities. • Coordinate with non-technical users to gather requirements. • Identify, summarize, and catalog data fidelity issues, and make recommendations to the client on how to resolve or mitigate the issues. • Apply industrial engineering methods (time studies, process mapping, Lean/Six Sigma, constraints analysis) to improve depot and sustainment processes. • Conduct capacity, throughput, and bottleneck analyses for depot maintenance, MRO, supply chain, and production environments to inform investment, resourcing, and modernization decisions. • Integrate data from ERP/MRO/maintenance systems into simulation models to generate actionable insights for forecasting, scheduling, and scenario analysis. • Collaborate with cross‑functional stakeholders (operations, supply chain, engineering, analytics) to design and test “what‑if” scenarios that support strategic planning and infrastructure decisions. • Prepare and deliver clear decision‑support briefings, visualizations, and recommendations for senior leaders regarding depot performance and modernization impacts. • Support adoption of advanced analytics and digital engineering approaches (e.g., predictive modeling, digital twins) to improve depot efficiency and readiness outcomes. • Develop trusted relationships with clients at all levels of the organization to obtain a more complete perspective and understanding of our clients’ mission, challenges, and goals to deliver tailored solutions. • Support business development efforts (e.g., responding to RFPs/RFIs, developing white papers, creating pitch decks and capability briefings, etc.).

Role Description As a Senior AI Data Engineer, you will build the data foundation that powers reporting, operational decision-making, automation, and AI across the company. You will design and deploy a modern AWS-based lakehouse architecture from the ground up, shaping how data is collected, governed, and delivered. Working across Engineering, Product, Finance, and Business Operations, you will establish the platform, standards, and engineering practices that transform data into a trusted enterprise capability. You'll make an impact by: - Building a modern, scalable enterprise data platform that supports analytics, business intelligence, and AI. - Creating trusted, governed data products that become the foundation for enterprise reporting and decision-making. - Designing reliable, automated data pipelines that integrate our core business systems into a single, scalable architecture. - Establishing engineering standards for data quality, governance, security, observability, and platform reliability. - Partnering with business and technology leaders to solve complex data challenges and enable new capabilities across the organization. - Helping define the long-term vision and technical direction of Sangoma's enterprise data platform. Qualifications - Experience building modern cloud-based data platforms. - Comfortable working across engineering, product, and business teams. - Experience with cloud data platforms in AWS or a similar cloud environment. - Building data pipelines using Python and SQL. - Data modeling, transformation, and orchestration tools. - Modern data warehouses such as Snowflake, Redshift, or equivalent platforms. - Git-based source control and CI/CD development practices. - Designing reliable, secure, and well-governed data solutions. Requirements - Experience in one or more of the following is a plus: - Snowflake or other cloud data platforms. - AI and machine learning data pipelines. - ERP and CRM platforms such as NetSuite and Salesforce. - Search and vector database technologies. Benefits - Extended Healthcare Plan (Medical, Disability, Dental & Vision). - RPP - Group RRSP. - Group Life - AD&D - Critical Illness Insurance. - Paid Time Off Benefits. - Work From Home - Flexible hours. - Entrepreneurial work environment partnered with high growth career opportunities.

Role Description As a Data Science Intern, you will support ongoing research initiatives that keep our enterprise products and our clients' hiring processes on the absolute cutting edge and leverage machine learning to shape the future of hiring. Partnering closely with our world-class data scientists and cross-functional partners in Product Science and Product and Technology, you will have the opportunity to build, validate, and scale machine learning models and agentic workflows. You will also conduct applied research on systems and processes to continuously advance our natural language processing (NLP) and ethical AI capabilities. Essential Duties and Responsibilities - Applied Research & Modeling - Support ongoing research initiatives that keep us on the cutting edge, including in agentic workflow development. - Work with machine learning models to build products and conduct research. - Write code to develop prototypes and utilities to extend or improve our product suite. - Data Preparation & Quality - Drive the preparation of data used to train deep learning models and score unstructured data. - Assure quality of our software in terms of content, scoring, and functionality. - Performance modeling and data analysis. Qualifications - Required - Bachelor’s degree required, with a Master’s degree or PhD in progress in a quantitative/programming field (Computer Science, Statistics, Mathematics, Engineering, Psychology, Economics, Data Science, or related). - Understanding of statistics, machine learning, and deep learning fundamentals. - Proficiency in Python, including common ML libraries (e.g., numpy, pandas, scikit-learn). - Experience with version control (e.g., Git). - Ability to analyze large datasets and draw meaningful, actionable insights. - Strong problem-solving skills — able to synthesize information from multiple sources to solve problems. - Team player with strong collaboration and communication skills; comfortable working closely with data scientists, engineers, and I/O Psychologists. - Self-motivated and able to work effectively with limited supervision; strong prioritization skills. - Preferred - Awareness of state of the art methods in machine learning, deep learning, NLP, and/or generative AI. - Exposure to large language models (LLMs) and generative AI (e.g., prompt engineering, fine-tuning, agentic design patterns, agentic evaluation methods). - Familiarity with SQL and database querying. - Familiarity with cloud platforms (AWS preferred) for ML workflows. Additional Information - All team members at Hirevue are expected to embody our core values, which are the H.E.A.R.T. of Hirevue: - Hero for our Customers - Enjoy the Journey - Always do the Right Thing - Reach Forwards - Take Action and Go - Hirevue takes security seriously and has measures in place to protect work-related data in a remote setting. - Our organization has implemented a BYOD (Bring Your Own Device) policy, ensuring personal apps and data are kept separate from work apps and data. - Hirevue is committed to equal treatment and opportunity in all aspects of recruitment, selection, and employment. - All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, or any other category protected under the law. - Hirevue is NOT currently hiring in: AK, HI, IA, ME, MS, NM or WV.