{"id":105682,"url":"https://alion.io/job/maneuver-marketing-data-engineer-cum-analyst","title":"Data Engineer","company":{"id":450362,"name":"Maneuver Marketing","domain":"maneuvermarketing.com","url":"https://alion.io/company/maneuver-marketing","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"B","score":75,"open_postings":5,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-11-10T06:52:35Z"}},"role":"Data Science","role_family":"Data Science","seniority":"middle","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":[],"countries":[],"hiring_countries":["IN","LK"],"hiring_countries_total":2,"salary":null,"salary_estimate":{"min_usd":15000,"max_usd":37000,"period":"year","method":"role_seniority_country_cell","sample_n":26},"experience_years_min":4,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"A/B Testing","optional":false},{"name":"Anomaly Detection","optional":false},{"name":"dbt","optional":false},{"name":"ETL/ELT","optional":false},{"name":"Fivetran","optional":false},{"name":"GDPR","optional":false},{"name":"Google BigQuery","optional":false},{"name":"Google Sheets","optional":false},{"name":"Looker","optional":false},{"name":"Metabase","optional":false},{"name":"Microsoft Excel","optional":false},{"name":"Power BI","optional":false},{"name":"SQL","optional":false},{"name":"Tableau","optional":false},{"name":"Azure SQL Database","optional":true},{"name":"MySQL","optional":true},{"name":"PostgreSQL","optional":true},{"name":"Slack","optional":true}],"status":"live","first_seen_at":"2026-08-13T08:17:40Z","employer_posted_date":"2026-08-13","last_verified_at":"2026-11-10T14:48:33Z","board_verified":true,"closed_at":null,"days_open":89,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":89},"description":"Build, Scale & Operate Leading DTC Brands alongside A-Players\nManeuver Marketing\nOur 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.\nIn 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.\nThese 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.\nThis is just the beginning of our journey, and you could be part of the next stage of our growth!\nWhat You'll Own\n1. Data Infrastructure & Pipeline Reliability\nKeep data flowing accurately, on time, and at cost.\nMonitor and respond to Daton pipeline alerts; track latency, freshness, and completeness across all source systems\n\nInvestigate pipeline failures and perform root cause analysis at the pipeline, QC/validation, and API/source system level\n\nMaintain and enhance existing data pipelines; with increasing ownership of new integrations as familiarity grows\n\nCoordinate with source system owners and vendors when issues originate upstream\n\nSupport query performance and warehouse cost efficiency, including implement table partitioning, clustering, and incremental load strategies\n\nMaintain documentation for all pipeline logic, schema changes, and incidents, with a continuously updated change log\n\n2. Data Quality & Validation\nBuild and maintain the quality layer that makes data trustworthy across the organization.\nOperate, maintain and improve automated QC null checks, duplicate detection, range/boundary checks, valid value checks, referential integrity, and business-logic validations for key KPIs\n\nPerform daily validation of critical metrics against source UIs (Shopify, GA4, Meta, Klaviyo, Google Ads, Loop, etc.)\n\nEnsure KPI consistency across raw, transformed, and reporting layers\n\nMaintain and improve anomaly detection for key tables and metrics\n\nProactively flag and escalate data integrity issues across teams\n\n3. Reporting, Dashboard & Metric Reliability\nMaintain reliable reporting and dashboard infrastructure\nMaintain and support dashboards and KPI trackers across Growth, Marketing, Product, Finance, and Operations.\n\nBuild or update dashboards based on established business requirements.\n\nMaintain and consistently apply agreed metric definitions and reporting standards across the organisation\n\nUse dbt or equivalent transformation tools to support clean and reliable analytical layers.\n\nUse AI tools and automation to improve reporting efficiency and reduce manual effort\n\n4. Growth & Marketing Analytics\nExecute well-defined Growth and Marketing analysis\nSupport analysis of paid-media and customer-performance metrics such as CAC, ROAS, MER, LTV, retention, and contribution margin\n\nValidate AI-surfaced growth trends and performance drivers, escalating notable findings to the Growth team.\n\nSupport evaluation of promotional performance and the impact of marketing initiatives.\n\nSupport interpretation of straightforward experiments and A/B tests, and communicate outcomes clearly.\n\n5. Product & Commercial Analysis\nSupport commercial decision-making with reliable analysis.\nMonitor and evaluate product performance across SKUs, channels, and markets\n\nSupport analysis of purchasing patterns, customer segmentation, pricing impact, and promotional effectiveness.\n\nSurface findings and support recommendations with quantitative analysis and clear business context.\n\nHighlight relevant trends or opportunities for Product and business teams to review\n\n6. Ad-Hoc Analysis & Decision Support\nRespond quickly and execute reliably.\nAnswer well-scoped analytical requests from cross-functional teams, using AI-agent outputs and existing reporting where appropriate to deliver clear, business-ready answers.\n\nValidate findings against source data before they reach stakeholders, flagging anything that looks off for review\n\nTranslate quantitative findings into clear, concise explanations for non-technical stakeholders\n\nCoordinate and manage VAs on data-related tasks - scoping work, reviewing outputs, maintaining quality\n\n7. Security, Compliance & Access Management\nProtect data and maintain regulatory alignment.\nFollow established GDPR, CCPA, and related data-handling controls\n\nApply established RBAC, PII-masking, and access-control practices in BigQuery and related systems\n\nEscalate suspected data-access, credential, or security issues promptly and support investigation where required\n\nTechnology Stack\nData Warehouse: Google BigQuery\n\nETL & Data Movement: Daton (primary), custom pipelines, dbt\n\nBI & Activation: Looker, Qlik, Segment (in progress)\n\nSource Systems: Shopify, GA4, Meta Ads, Google Ads, Klaviyo, Loop Subscriptions, Recart, Postscript, PayPal, and 20+ additional sources\n\nMonitoring & Alerting: Slack alerts, custom monitoring framework, email notifications\n\nRequired Qualifications\nTechnical\nWorking proficiency in Google BigQuery or a comparable cloud data warehouse, including querying, debugging, and validating data\n\nStrong working SQL, including joins, aggregations, CTEs, common window functions, and the ability to troubleshoot and verify queries.\n\nExperience with ETL/ELT tools such as Daton, Fivetran, or similar, and with pipeline monitoring.\n\nExperience with dbt or equivalent data transformation tools\n\nProficiency in at least one BI tool - Looker, Tableau, Power BI, Metabase, or similar\n\nE-commerce data experience (Shopify, GA4, ad platforms strongly preferred)\n\nStrong Excel / Google Sheets for ad-hoc analysis\n\nExperience using AI tools to support data engineering, troubleshooting, automation, or analytics workflows, with the ability to critically review generated outputs.\n\nAnalytical\nApplies structured reasoning to reliably execute well-scoped work, translating clear asks into accurate and complete outputs.\n\nValidates AI-generated reports and analysis against source data, flagging discrepancies, trends, risks and opportunities for review\n\nFamiliarity with subscription LTV, cohort analysis, funnel metrics, and A/B testing methodology\n\nProfessional\n4-6 years of experience spanning data engineering and analytics (or a strong hybrid track record)\n\nProven experience maintaining production data systems with strong troubleshooting and RCA skills\n\nClear communication with both technical and non-technical stakeholders\n\nProactive, ownership-driven mindset; ability to work independently in a remote setup\n\nStrong documentation discipline\n\nResponse availability: P0 within 2 hours, P1 within 4 hours, P2 within 24 hours on Singapore business days\n\nPreferred Qualifications\nExperience in eCommerce, DTC, subscription, or consumer goods\n\nFamiliarity with marketing KPIs - CAC, ROAS, MER, LTV:CAC, contribution margin\n\nExperience with A/B testing or experimentation frameworks\n\nFamiliarity with additional databases - PostgreSQL, MySQL, MongoDB, or Azure SQL\n\nExperience with AI-assisted data engineering, pipeline troubleshooting, workflow automation, monitoring, or reporting.\n\nExperience with advanced BigQuery optimization, partitioning, clustering, incremental models, or deeper dbt implementation.\n\nWhat Success Looks Like in Year One\nCritical data pipelines are monitored reliably, incidents are investigated systematically, and business teams can trust the data they rely on.\n\nSource-to-warehouse reconciliation improves, with discrepancies identified and resolved earlier\n\nRecurring reporting and QC processes become increasingly automated, with less manual intervention and more consistent validation.\n\nAI-generated SQL, diagnoses, and proposed fixes are consistently reviewed and validated before use.\n\nBusiness teams have a reliable data partner who can execute well-scoped analysis and validate AI-generated outputs accurately.\n\nAnalytical outputs are accurate, well-validated, and useful for business decision-making.\n\nProduct and promotional performance is reported consistently across SKUs and channels, with relevant findings surfaced for review.\n\nThe engineer progressively takes greater ownership of integrations, monitoring, automation, and AI-assisted engineering workflows\n\nLeadership makes faster, better-informed decisions through accessible, trustworthy data\n\nManeuver Marketing is an equal opportunity employer. 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