{"id":3075723,"url":"https://alion.io/job/forbes-data-engineer-2","title":"Data Engineer","company":{"id":8272,"name":"Forbes","domain":"forbes.com","url":"https://alion.io/company/forbes-advisor","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"A","score":85,"open_postings":12,"ghost_share":0.167,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":68,"computed_at":"2026-10-23T05:54:15Z"}},"role":"Data Science","role_family":"Data Science","seniority":"middle","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":15500,"max_usd":39000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":21},"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"ETL/ELT","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"SQL","optional":false}],"status":"live","first_seen_at":"2026-10-22T10:35:30Z","employer_posted_date":null,"last_verified_at":"2026-10-22T10:35:30Z","board_verified":false,"closed_at":null,"days_open":1,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":1},"description":"We are looking for a highly skilled Data Engineer (L3) with strong expertise in Python, SQL, data ingestion pipelines, and marketing data systems, with particular experience working with the Meta Ads ecosystem and digital marketing platforms. This role sits at the intersection of data engineering, marketing analytics, social/native advertising, and business intelligence, enabling scalable data pipelines, trusted datasets, lead-generation analytics, and data-driven business decision-making.\n\nThe candidate will have responsibilities across the following functions: \n\nData Engineering and Pipeline Development: \nDesign, build, and maintain robust data pipelines for marketing, social advertising, product, CRM, and customer data sources.\nDevelop scalable ETL/ELT workflows and Python/SQL-based data processing solutions.\nIntegrate data from APIs, event streams, batch systems, databases, and third-party marketing platforms.\nBuild reusable data ingestion frameworks and microservices to support growing marketing data requirements.\nEnsure high standards of data quality, reliability, scalability, and pipeline observability.\nOptimize data models and transformation layers for analytics, reporting, and downstream machine learning use cases.\nImplement automated data validation, monitoring, alerting, and failure-recovery mechanisms.\n\nMarketing and Ad Platform Data: \nOwn the ingestion, transformation, and modeling of data from Meta Ads (Facebook), Google Ads, and other digital marketing platforms.\nBuild scalable datasets supporting campaign performance, acquisition, lead generation, funnel analysis, attribution, and conversion tracking.\nDevelop campaign-level, ad-set-level, and ad-level datasets to analyze marketing performance.\nTrack and analyze metrics including impressions, clicks, CTR, CPC, CPM, conversions, CPA, ROAS, spend, and conversion rate.\nUnderstand digital advertising concepts, including campaign/ad set/ad hierarchy, bidding and optimization signals, attribution windows, pixel/event tracking, and conversion events.\nIntegrate advertising-platform data with CRM and customer datasets to evaluate lead quality, downstream conversion, revenue contribution, and customer acquisition efficiency.\n\nMarketing Analytics and Conversion Optimization: \nPerform campaign and conversion analytics to understand the complete customer journey from acquisition through lead generation and final conversion.\nBuild marketing funnel datasets to identify conversion drop-offs, high-performing channels, audiences, campaigns, and creatives.\nAnalyze Meta Ads and Google Ads performance to identify opportunities for improving acquisition efficiency and conversion rates.\nDevelop analytics solutions for lead scoring, lead quality measurement, customer segmentation, and campaign effectiveness.\nSupport attribution analysis by connecting marketing touchpoints with leads, conversions, and revenue outcomes.\nEnable data-driven decisions around audience targeting, campaign performance, channel efficiency, and marketing ROI.\nPartner with marketing and growth teams to define KPIs and success metrics tied to revenue, lead quality, acquisition cost, and conversion efficiency.\n\nBusiness Understanding and Cross-Functional Collaboration: \nTranslate requirements from Marketing, Growth, Product, Analytics, and Business teams into scalable data engineering solutions.\nWork closely with Data Leads and Engineering Leads to define architecture, data models, technical approaches, and implementation strategies.\nDevelop trusted datasets that enable self-service marketing analytics and business intelligence.\nCommunicate complex data and engineering concepts clearly to technical and non-technical stakeholders.\nUnderstand business objectives and ensure that data products directly support measurable business outcomes.\n\nData Quality, Governance, and Observability: \nImplement comprehensive data quality checks, validation frameworks, monitoring, and alerting across marketing data pipelines.\nEnsure consistency and reconciliation across Meta Ads, Google Ads, CRM, product, and internal data sources.\nMonitor data freshness, completeness, accuracy, and pipeline SLA adherence.\nEstablish clear documentation for data models, pipelines, source systems, transformations, and business definitions.\nBuild reliable and governed datasets that can be trusted for executive reporting, campaign optimization, and business decision-making.\nBuilt and maintained data pipelines to ingest and transform Meta Ads, Google Ads, CRM, and campaign performance data for marketing analytics and reporting.\nDeveloped analytics datasets for campaign, ad-set, and ad-level performance tracking, enabling detailed analysis of spend, impressions, clicks, CTR, CPC, conversions, CPA, and ROAS\n\nRequirements: \nStrong experience in Data Engineering using Python and SQL.\nHands-on experience building scalable ETL/ELT and API-based data ingestion pipelines.\nExperience working with marketing/ad-platform data, particularly Meta Ads.\nStrong understanding of campaign analytics, conversion funnels, attribution, lead generation, and marketing KPIs.\nAbility to connect technical data solutions with revenue, lead quality, customer acquisition, and conversion outcomes.\nStrong analytical and problem-solving skills with the ability to work across engineering, data, marketing, and growth teams.\nExperience building reliable, well-documented, and self-service-ready analytical datasets.\nSkills\nDatabricks, Google Ads, Google Analytics, Meta Ads, python","description_format":"text","description_chars":5515,"description_truncated":false,"requirements":{"experience_years_min":3,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Publishing","Digital Media","Financial News & Media"],"lifecycle":[{"event":"open","at":"2026-10-22T10:35:30Z"}],"visa":[],"liveness":{"score":90,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.903,"p_room":1,"age_days":0,"expected_fill_days":68,"reasons":["seen:0","velocity","win:early"],"computed_at":"2026-10-23T05:54:15Z"},"pay":null,"html_url":"https://alion.io/job/forbes-data-engineer-2","json_url":"https://alion.io/job/forbes-data-engineer-2.json","meta":{"generated_at":"2026-10-24T01:06:22Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per 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