Вакансія: Data Engineer — Azure / Data Factory / Azure Functions / Snowflake| Nature-based Solutions (в Херсон)

Загальна інформація про вакансію

<p><strong>About the Client &amp; Mission</strong></p> <p>Our client is the world’s largest environmental nonprofit focused on reforestation and sustainable development (Nature-based Solutions). We are building a modern cloud data platform on <strong>Azure and Snowflake</strong> that will serve as a single source of truth and enable faster, data-driven decision-making.</p> <p></p> <p>About the Initiative</p> <p>This role supports a Data Warehouse initiative focused on tangible delivery impact: trusted data, clear and scalable models, and fast release cycles (1–3 months) with well-defined SLAs. You’ll work in a collaborative setup across <strong>Data Engineering ↔ BI ↔ Product</strong>, often handling <strong>1–2 parallel workstreams </strong>with proactive risk and dependency management.</p> <p></p> <p><strong>Core Stack</strong></p> <ul><li><strong>ELT/DWH:</strong> Azure Data Factory + Azure Functions (Python) → Snowflake</li><li><strong>CI/CD:</strong> Azure DevOps pipelines + <strong>DL Sync</strong> (Snowflake objects and pipeline deployments)</li><li><strong>Primary data sources:</strong> CRM/ERP (Dynamics 365, Salesforce), MS SQL, API-based ingestion, CDC concepts</li><li><strong>Data formats:</strong> JSON, Parquet</li></ul> <p><strong>Team (our side)</strong></p> <p>Lead Data Engineering, PM, DevOps, QA.</p> <p></p> <p>Your Responsibilities</p> <ul><li>Design, build, and maintain <strong>incremental and full-refresh ELT pipelines</strong> (ADF + Azure Functions → Snowflake).</li><li>Develop and optimize <strong>Snowflake SQL</strong> for the DWH and data marts (Star Schema, incremental patterns, basic SCD2).</li><li>Build production-grade <strong>Python</strong> code in <strong>Azure Functions</strong> for ingestion, orchestration, and lightweight pre-processing.</li><li>Implement and maintain <strong>data quality controls</strong> (freshness, completeness, duplicates, late-arriving data).</li><li>Support <strong>CI/CD delivery</strong> for Snowflake objects and pipelines across dev/test/prod (Azure DevOps + DL Sync).</li><li>Contribute to documentation, <strong>best practices</strong>, and operational standards for the platform.</li><li>Communicate clearly and proactively: <strong>status → risk → options → next step</strong>, ensuring predictable delivery.</li></ul> <p>Requirements (Must-have)</p> <ul><li>4+ years in Data Engineering or related roles.</li><li>Strong <strong>Snowflake SQL</strong> (CTEs, window functions, COPY INTO, MERGE).</li><li>Hands-on experience with <strong>incremental loading</strong> (watermarks, merge patterns) and <strong>basic SCD2</strong> (effective dating / current flag).</li><li>Strong <strong>Python</strong> (production-ready code), including API integration (pagination, retries, error handling), logging, configuration, and secrets handling.</li><li>Solid experience with <strong>Azure Data Factory</strong> (pipelines, parameters, triggers) and <strong>Azure Functions</strong> (HTTP/Timer triggers, idempotency, retries).</li><li>Understanding of <strong>ELT/DWH modeling</strong> (Star Schema, fact/dimension design, performance implications of joins).</li><li>CI/CD familiarity: <strong>Azure DevOps</strong> and automated deployment practices for data platforms (DL Sync for Snowflake is a strong plus).</li><li>Strong communication skills and a proactive, accountable approach to teamwork.</li></ul> <p>Nice to Have</p> <ul><li><strong>PySpark</strong> (DataFrame API, joins, aggregations; general distributed processing understanding).</li><li>Experience with <strong>D365 / Salesforce</strong>, MS SQL sources, API-based ingestion, and CDC patterns.</li><li>Data governance/security basics, Agile/Scrum, and broader analytics tooling exposure.</li></ul> <p>Selection Process (Transparent &amp; Practical)</p> <p><strong>Stage 1 — Intro + TA + Short Tech Screen (40–60 min, Zoom):</strong></p> <ul><li>project context (multi-project setup, 1–3 month delivery cycles), must-haves for Azure/ELT, a short SQL/Python scenario;</li><li>soft skills &amp; culture match discussion covering: Proactive communication &amp; stakeholders, Critical thinking &amp; judgment, Problem solving &amp; systems thinking, Ownership &amp; maturity.</li></ul> <p><strong>Stage 2 — Deep-Dive Technical Interview (75–90 min, with 2 engineers):</strong><br />Live SQL (CTE/window + incremental load/SCD2 approach), PySpark mini-exercises, Azure lakehouse architecture discussion, plus a mini-case based on a real delivery situation.<br />No take-home task — we simulate day-to-day work during the session.</p> <p></p> <p>What We Offer</p> <ul><li>Competitive compensation.</li><li>Learning and growth alongside strong leaders, deepening expertise in Snowflake/ Azure / DWH.</li><li>Opportunity to expand your expertise over time across diverse, mission-driven &amp; AI projects.</li><li>Flexible work setup: remote / abroad / office (optional), gig contract (with an option to transition if needed).</li><li>Equipment and home-office support.</li><li><strong>36 paid days off per year:</strong> 20 vacation days + UA public holidays (and related days off, as applicable).</li><li><strong>Monthly benefit of the cafeteria:</strong> $25 to support your personal needs (learning, mental health support, etc.).</li><li>Performance reviews: ongoing feedback, compensation review after 12 months, then annually.</li><li>Paid sabbatical after 5 years with the company.</li></ul> <p><strong>P.S.</strong> Dear fellow Ukrainians,<br />we kindly ask you to apply for this role in a professional and well-reasoned manner, clearly highlighting the experience that is most relevant to the position.</p> <p>If you are unsure whether your background fully matches the requirements, please feel free to mention this openly in your application. This will not reduce your chances of being considered; it helps us review your profile fairly and prioritize candidates based on overall fit for the role.</p>