• Prepare and manage pre-stage files for backbook conversion activities • Support and execute data ingestion tasks in alignment with scheduled project events, including key mock events • Monitor and ensure data ingestion completion within defined SLA windows • Troubleshoot and resolve ingestion issues promptly to maintain data integrity and project timelines • Maintain detailed documentation of ingestion processes, schedules, and completion statuses • Proactive problem-solving approach

• Build and own end-to-end data pipelines in Snowflake — from raw ingestion through transformation to serving layers for AI products • Partner with ML engineers and data scientists to build and maintain AI-specific data infrastructure • Consolidate fragmented data sources across the organization into reliable, automated pipelines • Design scalable data models and marts that serve both analytics and ML feature engineering • Support rapid iteration on new data products and features in a fast-moving environment • Collaborate cross-functionally with analytics, data science, and product teams to translate requirements into data solutions • Proactively monitor and resolve data quality issues, optimizing for cost and performance • Employ AI technologies to enhance and optimize business processes • Utilize and leverage Power Digital's Nova ecosystem as it relates to your department • Use AI coding tools as part of your daily development workflow to accelerate pipeline development and data quality work

• Build and own end-to-end data pipelines in Snowflake — from raw ingestion through transformation to serving layers for AI products • Partner with ML engineers and data scientists to build and maintain AI-specific data infrastructure • Consolidate fragmented data sources across the organization into reliable, automated pipelines • Design scalable data models and marts that serve both analytics and ML feature engineering • Support rapid iteration on new data products and features in a fast-moving environment • Collaborate cross-functionally with analytics, data science, and product teams to translate requirements into data solutions • Proactively monitor and resolve data quality issues, optimizing for cost and performance • Employ AI technologies to enhance and optimize business processes • Utilize and leverage Power Digital's Nova ecosystem as it relates to your department • Use AI coding tools as part of your daily development workflow to accelerate pipeline development and data quality work

Role Description As an Intermediate Data Engineer on our Data team, you'll be closely collaborating with engineering teams, designers, and stakeholders across the company to solve data problems that span the full stack — from pipelines and infrastructure to customer-facing data products. You'll be part of a small, dynamic team — the problems you'll work on won't always come with a neat brief, and we need someone who takes ownership and runs with it. Reporting to a Principal Data Engineer, your work will directly impact both internal decision-making and customer success. Your goal will be to help shape our data products and infrastructure to have a top-line impact on our company goals. Here's how you'll accomplish this: - Take ownership of undefined problems — dig in, scope the work, and ship solutions, whether that's a pipeline, a dashboard, a tracking implementation, or something we haven't thought of yet. - Ship customer-facing analytics dashboards and features — this is production code, not just SQL and charts. - Work directly with engineering teams to implement tracking services across our products. - Interface with teams across the company who need data solutions, translating fuzzy requirements into concrete deliverables. - Raise the bar on technical quality across the data team — through architecture decisions, reducing tech debt, code reviews, and bringing software development best practices to a high-performing data team. - Work with your team to conduct new technology research; bring fresh ideas and concepts to bear on how we integrate AI into our data workflows. Qualifications - 3–5 years of experience working across data engineering, full-stack development, analytics, or some combination. - Solid data engineering experience — you've built and maintained pipelines, worked with warehouses, and understand data modeling and quality. - Full-stack development experience — you've shipped production code that users interact with, not just internal tooling. - Hands-on experience with dbt and BigQuery or similar stacks (Snowflake, Databricks, etc.). - Self-motivated and resourceful — you ship things, communicate clearly across technical and non-technical teams, and are always looking to level up. - Comfortable working across the stack and across disciplines; you don't need to be an expert everywhere, but you're not afraid to jump in. - Loves to learn and grow — they’ve found (and keep looking for) ways to level up their skills in this field. Requirements - Experience with LLM/agent development — building AI-powered data products, automated workflows, or integrating LLMs into data pipelines and analytics. - Experience with GCP and Terraform. - Any exposure to ML. - Experience white-labeling a BI tool or embedding analytics into a customer-facing product. - Experience building or maintaining event tracking systems. - Familiarity with modern data stack tools beyond dbt/BigQuery (Airflow, Dagster, etc.). - Experience working in startup-style environments or autonomous teams within larger orgs. - Experience working in a SaaS environment. Benefits - Competitive compensation package including base salary, equity, team-wide bonuses, and an Employee Share Purchase Plan. - Flexible Paid Time Off to maintain mental and physical health — minimum 4 weeks of vacation, plus Thinker Holidays and time off for the December holiday season. - Comprehensive health benefits starting on Day 1, including health, vision, and dental coverage for you and your family, $3,000 for mental health care, and additional health spending accounts. - Generous parental leave top-ups for up to 32 weeks, fertility coverage, and personalized return to work options. - 3 Paid Volunteer Days each year to give back to your community. - Flexible Work — choose to work from home, at our Vancouver HQ, or anywhere with wifi. - An annual $2,000 CAD Learn and Grow fund for conferences, seminars, or courses, plus training, mentorship, coaching, and internal promotion opportunities. - A home office setup with a company-owned Macbook Pro and budget for ergonomic accessories. - A welcoming environment where you can bring your whole self to work.