Data Engineer

Job description

# **Build, Scale & Operate Leading DTC Brands alongside A-Players** ## **Maneuver Marketing** Our Vision, Mission & Success are fuelled by our commitment to be a driving force of positive change to the health of everyday consumers, providing conscious, high-quality & innovative supplement products. In just 5 years, we kicked off our own DTC Health & Wellness brand from scratch and scaled it to USD$100M+ in annual sales, serving more than 3,000,000 customers worldwide with an average of 4,000 daily orders across 9 SKUs. These results caught the attention of [The Financial Times](https://www.ft.com/high-growth-asia-pacific-ranking-2023), as they ranked us among APACs top High-Growth Companies. We have also been awarded [2nd place on the E50 Awards](https://www.sph.com.sg/media-centre/media-releases/fifty-local-smes-honoured-for-pioneering-success-and-innovation-at-30th-e50-awards/), jointly organised by The Business Times and KPMG in Singapore. **This is just the beginning of our journey, and you could be part of the next stage of our growth!** ## **What You'll Own** ### **1. Data Infrastructure & Pipeline Reliability** Keep data flowing accurately, on time, and at cost. - Monitor, build, and respond to Daton pipeline alerts; track latency, freshness, and completeness across all source systems - Investigate pipeline failures and perform root cause analysis at the pipeline, QC/validation, and API/source system level - Create and enhance data pipelines; onboard new platform integrations and implement logic changes to existing ones - Coordinate with source system owners and vendors when issues originate upstream - Optimize query performance and warehouse costs; implement table partitioning, clustering, and incremental load strategies - Maintain documentation for all pipeline logic, schema changes, and incidents, with a continuously updated change log ### **2. Data Quality & Validation** Build and maintain the quality layer that makes data trustworthy across the organization. - Design and maintain automated QC checks: null checks, duplicate detection, range/boundary checks, valid value checks, referential integrity, and business-logic validations for key KPIs - Perform daily validation of critical metrics against source UIs (Shopify, GA4, Meta, Klaviyo, Google Ads, Loop, etc.) - Ensure KPI consistency across raw, transformed, and reporting layers - Implement anomaly detection for key tables and metrics - Proactively flag and resolve data integrity issues across teams ### **3. Reporting & Dashboard Ownership** Own the company-wide reporting infrastructure. - Build, maintain, and evolve dashboards and KPI trackers for Growth, Marketing, Product, Finance, and Operations - Design and manage executive-level dashboards that support leadership decision-making - Own metric definitions, documentation, and reporting standards across the organization - Leverage dbt (or equivalent) to maintain clean, reliable analytical layers - Use AI tools and automation to improve reporting efficiency and reduce manual effort ### **4. Growth & Marketing Analytics** Be the analytical partner the Growth team relies on. - Analyze performance across paid media channels — CAC, ROAS, MER, LTV, retention, and contribution margin - Conduct deep-dive analyses to identify growth opportunities and performance drivers - Evaluate promotional performance and measure the impact of marketing initiatives - Support experimentation and A/B testing: help design tests, interpret results, and communicate outcomes to stakeholders ### **5. Product & Commercial Analysis** Turn data into commercial decisions. - Monitor and evaluate product performance across SKUs, channels, and markets - Analyze purchasing patterns, customer segmentation, pricing impact, and promotional effectiveness - Provide recommendations based on quantitative analysis and business impact - Identify opportunities to improve product assortment, pricing decisions, and inventory allocation ### **6. Ad-Hoc Analysis & Decision Support** Respond fast and think clearly under ambiguity. - Answer analytical requests from cross-functional teams with structured, business-ready outputs - Investigate business challenges, identify root causes, and present actionable recommendations - Translate complex datasets and quantitative findings into clear narratives for non-technical stakeholders - Coordinate and manage VAs on data-related tasks — scoping work, reviewing outputs, maintaining quality ### **7. Security, Compliance & Access Management** Protect data and maintain regulatory alignment. - Maintain GDPR, CCPA, and related compliance controls - Manage RBAC and column-level security in BigQuery; ensure PII masking and access restrictions - Respond to security incidents related to data access or credentials ## **Technology Stack** - **Data Warehouse:** Google BigQuery - **ETL & Data Movement:** Daton (primary), custom pipelines, dbt - **BI & Activation:** Looker, Qlik, Segment *(in progress)* - **Source Systems:** Shopify, GA4, Meta Ads, Google Ads, Klaviyo, Loop Subscriptions, Recart, Postscript, PayPal, and 20+ additional sources - **Monitoring & Alerting:** Slack alerts, custom monitoring framework, email notifications ## **Required Qualifications** ### **Technical** - Strong Google BigQuery expertise — SQL optimization, partitioning, clustering - Advanced SQL — window functions, CTEs, complex joins, query optimization - Experience with ETL tools (Daton, Fivetran, or similar) and pipeline monitoring - Experience with dbt or equivalent data transformation tools - Proficiency in at least one BI tool — Looker, Tableau, Power BI, Metabase, or similar - E-commerce data experience (Shopify, GA4, ad platforms strongly preferred) - Strong Excel / Google Sheets for ad-hoc analysis - Experience using AI tools for automation, workflow improvement, or analytics ### **Analytical** - Structured problem-solving — breaks ambiguous business questions into clear analytical frameworks - Independently identifies trends, risks, and opportunities without a specific brief - Familiarity with subscription LTV, cohort analysis, funnel metrics, and A/B testing methodology ### **Professional** - 4–6 years of experience spanning data engineering and analytics (or a strong hybrid track record) - Proven experience maintaining production data systems with strong troubleshooting and RCA skills - Clear communication with both technical and non-technical stakeholders - Proactive, ownership-driven mindset; ability to work independently in a remote setup - Strong documentation discipline - Response availability: P0 within 2 hours, P1 within 4 hours, P2 within 24 hours on Singapore business days ## **Preferred Qualifications** - Experience in eCommerce, DTC, subscription, or consumer goods - Proficiency in Python for data manipulation or workflow automation - Familiarity with marketing KPIs — CAC, ROAS, MER, LTV:CAC, contribution margin - Experience with A/B testing or experimentation frameworks - Familiarity with additional databases — PostgreSQL, MySQL, MongoDB, or Azure SQL - Experience implementing AI-powered analytics workflows or reporting automation ## **What Success Looks Like in Year One** - Data pipelines are monitored proactively, incidents are resolved fast, and business teams trust the data they rely on - Reporting across key business functions is automated and accurate, with reduced manual effort and clear metric ownership - The Growth team has a strong analytical partner — from campaign performance to retention to product analytics - Insights directly influence marketing efficiency, budget allocation, and promotional decisions - Product and promotional performance is visible across SKUs and channels, with clear recommendations for action - Leadership makes faster, better-informed decisions through accessible, trustworthy data *Maneuver Marketing is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.*