Data Engineer

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.