• Design and manage Feed the Children’s modern cloud data infrastructure, with ownership of Microsoft Fabric and Azure. • Lead the stand up and evolution of Feed the Children’s Fabric platform and serve as its primary Product Owner. • Collaborate closely with ERP and CRM teams, and coordinate with Data Governance leadership. • Ensure seamless integration of data and data flows from various sources, including ERP systems, CRM systems, DHIS2, external datasets and APIs, and other business applications. • Implement processes and tools to ensure high data quality and consistency across the organization.

• 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.

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.).