Senior Analytics Engineer

As the largest global shared micromobility business, Lime is on a mission to build a future where transportation is shared, affordable and carbon-free. A Time Magazine 100 Most Influential Company, Lime has powered more than one billion rides in close to 30 countries across five continents, spurring a new generation of clean alternatives to car ownership. Learn more at li.me.

Lime is hiring a Sr. Analytics Engineer to join our Corp Tech Data and Integrations Team. You will report to Lime’s Corp Tech Analytics Manager in the Enterprise Engineering space and partner closely with Finance & Accounting leadership to design systems with the highest levels of governance — ensuring our business performance and financial reporting data can withstand rigorous external audits.

This is a remote position with a requirement for candidates to reside in Canada to maintain effective collaboration across teams.

What You’ll Do:

• Design the long-term technical vision for Lime’s Corporate Data Warehouse to build a robust, scalable Finance-grade semantic layer that supports Finance, Accounting, and internal Corp Tech analytics initiatives.
• Own the Finance data modeling strategy across core systems, including NetSuite and sub-ledgers, ensuring consistent definitions for key measures (revenue, COGS, asset balances, depreciation, accruals, close KPIs).
• Define and enforce standards for dbt modeling, SQL style, CI/CD workflows, documentation, and automated testing — so the entire team operates with audit-grade precision.
• Build reconciliation-ready datasets that support month-end close and audits: control totals, roll-forwards, sub-ledger to GL tie-outs, variance explanations, and transparent lineage.
• Enforce strict data governance and controls: data ownership, glossary, lineage, change management, access patterns, and automated data quality validation aligned to Finance expectations.
• Partner with Finance stakeholders (Accounting/FP&A/Finance Ops) to ensure analytics solutions support month-end close workflows, audit evidence needs, and stakeholder trust.
• Evaluate and integrate orchestration & automation capabilities that reduce manual intervention and operational risk across ingestion → transformation → reporting pipelines (including alerting/observability and SLA monitoring).
• Mentor Analytics Engineers on the team through design review, code review, and pairing — raising the bar on modeling, testing, and operational rigor.

About You:

• Bachelor’s or Master’s degree in Computer Science, Data Engineering, or a related technical field.
• 5+ years in analytics engineering / data warehousing, with a track record of designing architectures that scale in fast-growing environments.
• Strong backend instincts: you think in data contracts, idempotency, late-arriving data, reprocessing, control totals, and lineage — not just dashboards.
• Demonstrated ability to influence technical and non-technical stakeholders at the Director/VP level, navigating conflicting requirements to land the best long-term solution.

Technical Requirements

• Cloud & warehouse: 5+ years building and scaling data stacks on cloud providers (AWS preferred), including deep production experience with Snowflake — warehouse sizing, clustering, incremental strategies, query profiling, and cost/performance trade-offs.
• SQL & Python: expert-level, high-performance SQL, plus strong Python for transformation, tooling, and automation. You can develop and debug complex transformations and explain why they perform the way they do.
• dbt: deep expertise (macros, packages, performance patterns, project structuring) and a clear philosophy on how to run large-scale dbt programs with maintainability and reliability.
• Data modeling: dimensional modeling, ELT pipeline design, and semantic layer design for Finance-grade reporting.
• Orchestration: workflow orchestration tools such as Airflow — DAG design, dependency management, backfills, retries, SLAs, and how orchestration interacts with the transformation layer.
• Data ops: CI/CD for data pipelines, version-controlled schemas, automated testing, code review standards, and release management.
• Reliability & observability: freshness and volume monitoring, anomaly detection on control totals, lineage tracking, alerting, runbooks, and clear ownership.
• Governance tooling: modern data governance tools and practices — cataloging, lineage, PII masking, and role-based