Data Engineer

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, as they ranked us among APACs top High-Growth Companies. We have also been awarded 2nd place on the 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 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

• Maintain and enhance existing data pipelines; with increasing ownership of new integrations as familiarity grows

• Coordinate with source system owners and vendors when issues originate upstream

• Support query performance and warehouse cost efficiency, including 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.

• Operate, maintain and improve automated QC 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

• Maintain and improve anomaly detection for key tables and metrics

• Proactively flag and escalate data integrity issues across teams

3. Reporting, Dashboard & Metric Reliability

Maintain reliable reporting and dashboard infrastructure

• Maintain and support dashboards and KPI trackers across Growth, Marketing, Product, Finance, and Operations.

• Build or update dashboards based on established business requirements.

• Maintain and consistently apply agreed metric definitions and reporting standards across the organisation

• Use dbt or equivalent transformation tools to support clean and reliable analytical layers.

• Use AI tools and automation to improve reporting efficiency and reduce manual effort

4. Growth & Marketing Analytics

Execute well-defined Growth and Marketing analysis

• Support analysis of paid-media and customer-performance metrics such as CAC, ROAS, MER, LTV, retention, and contribution margin

• Validate AI-surfaced growth trends and performance drivers, escalating notable findings to the Growth team.

• Support evaluation of promotional performance and the impact of marketing initiatives.

• Support interpretation of straightforward experiments and A/B tests, and communicate outcomes clearly.

5. Product & Commercial Analysis

Support commercial decision-making with reliable analysis.

• Monitor and evaluate product performance across SKUs, channels, and markets

• Support analysis of purchasing patterns, customer segmentation, pricing impact, and promotional effectiveness.

• Surface findings and support recommendations with quantitative analysis and clear business context.

• Highlight relevant trends or opportunities for Product and business teams to review

6. Ad-Hoc Analysis & Decision Support

Respond quickly and execute reliably.

• Answer well-scoped analytical requests from cross-functional teams, using AI-agent outputs and existing reporting where appropriate to deliver clear, business-ready answers.

• Validate findings against source data before they reach stakeholders, flagging anything that looks off for review

• Translate quantitative findings into clear, concise explanations 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.

• Follow established GDPR, CCPA, and related data-handling controls

• Apply established RBAC, PII-masking, and access-control practices in BigQuery and related systems

• Escalate suspected data-access, credential, or security issues promptly and support investigation where required

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

• Working proficiency in Google BigQuery or a comparable cloud data warehouse, including querying, debugging, and validating data

• Strong working SQL, including joins, aggregations, CTEs, common window functions, and the ability to troubleshoot and verify queries.

• Experience with ETL/ELT tools such as Daton, Fivetran, or similar, and with 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 to support data engineering, troubleshooting, automation, or analytics workflows, with the ability to critically review generated outputs.

Analytical

• Applies structured reasoning to reliably execute well-scoped work, translating clear asks into accurate and complete outputs.

• Validates AI-generated reports and analysis against source data, flagging discrepancies, trends, risks and opportunities for review

• 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

• 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 with AI-assisted data engineering, pipeline troubleshooting, workflow automation, monitoring, or reporting.

• Experience with advanced BigQuery optimization, partitioning, clustering, incremental models, or deeper dbt implementation.

What Success Looks Like in Year One

• Critical data pipelines are monitored reliably, incidents are investigated systematically, and business teams can trust the data they rely on.

• Source-to-warehouse reconciliation improves, with discrepancies identified and resolved earlier

• Recurring reporting and QC processes become increasingly automated, with less manual intervention and more consistent validation.

• AI-generated SQL, diagnoses, and proposed fixes are consistently reviewed and validated before use.

• Business teams have a reliable data partner who can execute well-scoped analysis and validate AI-generated outputs accurately.

• Analytical outputs are accurate, well-validated, and useful for business decision-making.

• Product and promotional performance is reported consistently across SKUs and channels, with relevant findings surfaced for review.

• The engineer progressively takes greater ownership of integrations, monitoring, automation, and AI-assisted engineering workflows

• 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.