Job description

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 access control. • Distributed & streaming: working familiarity with Spark, Flink, or Kafka, and with CDC/ingestion patterns supporting batch and near-real-time analytics. • Infrastructure: experience with Iceberg, Debezium, or Infrastructure-as-Code tools like Terraform for managing data infrastructure. Finance & Accounting Requirements This role sits at the intersection of engineering and Finance. You do not need to be an accountant, but you do need to speak the language of the close and design systems that hold up under audit. • ERP fundamentals (NetSuite preferred): transactions and their GL impact, accounting periods and close calendars, dimensionality (subsidiary / department / class / location), and how upstream operational events roll into Finance reporting. • General Ledger & sub-ledgers: comfort navigating GL and sub-ledger complexity, and modeling sub-ledger-to-GL reconciliations with clear, defensible “single source of truth” definitions. • Core financial measures: consistent definition and modeling of revenue, COGS, asset balances, depreciation, accruals, and month-end close KPIs. • Fixed Assets / FAM: asset lifecycle data, depreciation logic (e.g., straight-line), asset resets and adjustments, roll-forwards, and auditability of asset balances. • Close & audit support: building reconciliation-ready datasets — control totals, roll-forwards, sub-ledger-to-GL tie-outs, variance explanations, and transparent lineage that can withstand rigorous external audit. • Planning & forecasting: familiarity with planning models (e.g., Anaplan) and how plan vs. actuals alignment should be modeled and governed. • Controls & governance: working within strict internal controls (SOX, audit readiness, IPO readiness, or other regulated environments) — data ownership, change management, access patterns, and evidence trails. • Finance partnership: the ability to work directly with Accounting, FP&A, and Finance Ops to translate close workflows and audit evidence needs into concrete data requirements. Preferred Experience: • Experience implementing data quality frameworks (unit/integration tests, anomaly detection on control totals, freshness/SLA checks) and operationalizing them (alerts, runbooks, ownership). • Experience integrating ERP and planning platforms (NetSuite, Anaplan) into a governed warehouse via APIs or CDC. The base salary range listed reflects what Lime reasonably expects to offer for this role, with the final base salary determined by objective factors such as the candidate’s location and relevant skills and experience. Depending on the position, the total compensation package may also include discretionary annual performance bonus opportunities and equity, subject to applicable plan terms and eligibility requirements. #LI-PP1 #LI-Remote If you want to make an impact, Lime is the place for you. Not sure if you meet all the qualifications? If this role excites you we encourage you to apply. Explore all opportunities on our career page. Lime is proud to be an Equal Opportunity Employer. We believe different perspectives help us grow and achieve more. That’s why we’re dedicated to building and developing a team that reflects a wider range of backgrounds, abilities, identities, and experiences. If you require a reasonable accommodation during the application or hiring process, please email recruiting-operations@li.me for assistance.

Requirements

Must-haves

This is a remote position with a requirement for candidates to reside in Canada to maintain effective collaboration across teams.
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.
Mentor Analytics Engineers on the team through design review, code review, and pairing — raising the bar on modeling, testing, and operational rigor.
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
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 access control.
Distributed & streaming: working familiarity with Spark, Flink, or Kafka, and with CDC/ingestion patterns supporting batch and near-real-time analytics.
Infrastructure: experience with Iceberg, Debezium, or Infrastructure-as-Code tools like Terraform for managing data infrastructure.

Nice-to-haves

Experience implementing data quality frameworks (unit/integration tests, anomaly detection on control totals, freshness/SLA checks) and operationalizing them (alerts, runbooks, ownership).
Experience integrating ERP and planning platforms (NetSuite, Anaplan) into a governed warehouse via APIs or CDC.

Application prep

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Keywords to mirror

design systemssqlci/cdawspython

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Benefits

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