{"id":946753,"url":"https://alion.io/job/marcohire-data-analyst","title":"Data Analyst","company":{"id":679693,"name":"MarcoHire","domain":"marcohire.com","url":"https://alion.io/company/marcohire","size_band":"51-200","is_staffing_agency":false,"employer_type":"staffing","is_intermediary":true,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"B","score":75,"open_postings":3,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-11-07T05:47:08Z"}},"role":"Analytics","role_family":"Analytics","seniority":"middle","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_regions","remote_scope_basis":"posting_text","remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":[],"countries":[],"hiring_countries":["AR","BR","CO","MX","AI","AG","AW","BS","BB","BZ","BM","BO","VG","BQ","KY","CL","CR","CW","DM","DO","EC","SV","FK","GF","GD","GP","GT","GY","HT","HN","JM","MQ","MS","NI","PA","PY","PE","PR","KN","LC"],"hiring_countries_total":49,"salary":{"min":2000,"max":3000,"currency":"USD","period":"month","gross":null,"usd_annual":36000},"salary_estimate":null,"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Amazon Redshift","optional":false},{"name":"ETL/ELT","optional":false},{"name":"Fivetran","optional":false},{"name":"Google BigQuery","optional":false},{"name":"Looker","optional":false},{"name":"Python","optional":false},{"name":"QuickBooks","optional":false},{"name":"Snowflake","optional":false},{"name":"SQL","optional":false}],"status":"live","first_seen_at":"2026-09-15T14:03:04Z","employer_posted_date":"2026-09-15","last_verified_at":"2026-11-07T14:53:51Z","board_verified":true,"closed_at":null,"days_open":53,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":53},"description":"Type: Full-Time (Contractor)\nLocation: Remote (LatAm)\nWork Timezone: U.S. / North America\nCompensation: $2,000-$3,000/month USD\nAbout Marco: Marco connects the top 1% of global professionals with ambitious, fast-growing companies. We believe the future of work is borderless-and that exceptional people come from anywhere.\nOur Client:\nOur client is DSLRPros (Enterprise UAS, LLC), a leading U.S. commercial drone and UAS reseller serving public safety agencies, enterprises, and government buyers. The company runs three brands and nine departments on an all-SaaS stack-Shopify Plus, Pipedrive, QuickBooks Online, ShipStation, PandaDoc, Rippling, Google Analytics, and Asana-and is growing through both organic expansion and acquisition.\nA company-wide KPI framework and tracker are already in place, with department leads entering monthly actuals after each month close. What does not exist yet is a reporting layer: there is no BI tool, no central data warehouse, and no automated pipeline between the systems. That is what this role is here to build.\nThe Role:\nThey are hiring a Data Analyst to stand up business intelligence at DSLRPros from the ground up and then run it. You will architect and build the reporting stack-centralizing data out of eight SaaS systems and into Looker-then own the company-wide KPI framework and the reporting that sits on top of it.\nThis is a build-then-operate role, and it is deliberately two-sided. The first half is technical: pipelines, data models, metric definitions, dashboards. The second half is communication. You will present to the executive team monthly and to department teams on a regular cadence, in plain language, with a clear point of view on what the numbers mean and what should change. A dashboard nobody acts on is a failed dashboard here.\nYou report directly to the Chief Operating Officer and work with every department lead in the business.\nWhat You Will Own:\nStanding up the BI stack\nArchitect and implement the reporting stack from zero: source connections, a central data layer, and Looker as the reporting front end\n\nSelect and implement the ETL/ingestion approach (Fivetran, Airbyte, native connectors, or direct API) for each source system, with a clear case for the cost and trade-offs of each\n\nBuild clean, documented data models that other people can read and extend\n\nMake monthly reporting a review step rather than a data-entry exercise\n\nData integrity\nPull and consolidate data across Shopify Plus, Pipedrive, QuickBooks Online, ShipStation, PandaDoc, Rippling, Google Analytics, and Asana\n\nEstablish one agreed definition for every metric, so revenue, margin, and pipeline mean the same thing in every report\n\nReconcile discrepancies between systems and find the source of the difference rather than reporting around it\n\nFlag data quality problems and work with department leads to fix them at the source\n\nMeasurement and analysis\nOwn the company-wide KPI framework and tracker: maintain the definitions, keep targets current, and make sure each department is measured on the right things\n\nProduce monthly performance reporting for all nine departments across three brands\n\nGo past the number itself-explain the movement, identify the driver, and flag what leadership should be paying attention to\n\nRun ad hoc analysis for the executive team on product line profitability, customer segments, sales performance, and operational throughput\n\nReporting and presentation\nBuild and maintain Looker dashboards that department leads open themselves instead of requesting a report\n\nProduce a monthly executive package that is clear enough to be read cold and short enough to actually get read\n\nPresent findings live to the executive team monthly, and to department teams on a regular cadence\n\nTurn analysis into specific recommendations rather than leaving the audience to interpret it\n\nDocumentation and scale\nDocument every data source, definition, model, and process so reporting does not depend on one person\n\nBuild reporting that scales as the company adds departments, brands, and acquisitions\n\nRecommend and implement improvements to the stack as the business grows\n\nWho You Are:\nYou have built a reporting stack before, not just worked inside one someone else built\n\nYou are equally comfortable in a SQL editor and in front of a leadership team\n\nYou think in definitions-you know that \"revenue\" meaning three different things in three systems is the real problem, not a rounding error\n\nYou make a recommendation rather than handing over a chart and waiting\n\nYou are self-directed and work well with a U.S. team, with a bias toward asking early rather than guessing\n\nYou are fluent in English, with clear verbal and written communication\n\nYou are comfortable being the only data person in the building, and you document accordingly\n\nExperience We're Looking For:\nRequired\n3-5 years in a data analyst, business intelligence, or analytics engineering role\n\nStrong SQL, and hands-on experience building or managing a cloud data warehouse (BigQuery, Snowflake, Redshift, or similar)\n\nExperience implementing ETL/ingestion from SaaS APIs-Fivetran, Airbyte, or equivalent\n\nDemonstrated experience building dashboards in Looker or Looker Studio from scratch, including the data model underneath them\n\nAdvanced spreadsheet skills-you can take messy data from several systems and get to a number people can trust\n\nProven ability to explain data to people who are not analysts, and to present to executives\n\nPreferred\nE-commerce and CRM data experience, particularly Shopify and a sales CRM such as Pipedrive\n\nFamiliarity with accounting data and financial concepts such as margin, COGS, and 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