Job Type: Full-time

Role: Data Engineer L3Experience: 7+ yearsWork-mode: RemoteEmployement type: Full-timeBudget: 38 LPARole description:We are looking for a highly skilled Data Engineer (L3) with strong expertise in Python, data ingestion pipelines and marketing data systems, particularly with the Meta Ads ecosystem. This role sits at the intersection of data engineering and social/native platforms, enabling scalable data pipelines, highquality datasets, and lead generation and business decision-making.This role goes beyond building pipelines - will be responsible for:Designing scalable data architectureDriving business outcomes (revenue, lead quality, conversion efficiency)Owning how data is used, trusted and acted uponThe ideal candidate will not manage campaign buying or bidding directly but must clearlyunderstand ad platform mechanics, attribution models and lead quality scores and will workclosely with the Data Lead / Engineering Lead, acting as a key contributor in shapingsolutions, making technical decisions, and delivering high-impact data products.Responsibilities:Data Engineering & Pipelines:Design, build, and maintain robust data data pipelines for social marketing and product data sources (APIs, event streams, batch systems)Develop scalable ETL/ELT workflows / microservices using Python and SQLEnsure high data quality, reliability and observability across pipelinesOptimize data models for analytics and reporting use casesMarketing & Ad Platform Data:1. Own ingestion and modeling of data from Meta Ads (Facebook) and other digitalmarketing platforms2. Build datasets that support:Campaign performance trackingLead funnel analysisAttribution and conversion tracking3.



Understand key concepts such as:Campaign structure (campaign/ad set/ad level)Bidding & optimization signalsAttribution windowsPixel / event trackingBusiness Understanding & Collaboration:Translate business requirements from marketing, growth and product teams into scalable data solutionsDefine success metrics tied to revenue and performanceEnable self-serve analytics through well-structured datasetsData Quality & Governance:Implement validation checks, monitoring and alerting for pipelinesEnsure consistency across different marketing data sourcesMaintain clear documentation of data models and pipelinesBusiness Collaboration & Use Case Ownership:Work closely with marketing, growth, and analytics teams to:➢ Understand real-world use cases➢ Define success metrics tied to revenue and performanceOwn key use cases such as:➢ Lead funnel optimization➢ Campaign attribution➢ Revenue reporting and forecastingEnsure data enables decision-making, not just reportingEngineering Standards & Best Practices:Design and implement modular, reusable microservices that enable the scalable development of data products.Drive standardization through well-architected,



loosely coupled services that can be leveraged across multiple use cases.Uphold high standards in:Code quality and modularityPipeline reliability and monitoringDocumentation and data contractsContribute to shared frameworks and reusable componentsPromote best practices across the data engineering teamRequired Skills & Qualifications:1. Core Technical SkillsStrong proficiency in Python (must-have)Advanced SQL skills for large-scale data processingHands-on experience with data ingestion from APIs (rate limits, pagination, retries)Experience with data orchestration tools (e.g., Airflow or equivalent)Familiarity with cloud data platforms (BigQuery, etc.)Experience building scalable data ingestion systemsFamiliarity with microservices-style or modular data systemsStrong understanding of performance and cost optimization2. Ad Platform Knowledge:Solid understanding of Meta Ads platform fundamentalsFamiliarity with:Campaign hierarchy and metrics (CTR, CPC, CPA, ROAS)Conversion tracking and attribution modelsLead generation workflows and funnel metricsAbility to interpret marketing data beyond surface-level metricsExposure to event tracking systems (GA4, Snowplow, etc)Positive to Have:Experience with other ad platforms (Google Ads, Bing Ads, etc.)Knowledge of data modeling best practices (e.g., star schema, dbt)Experience with real-time or near real-time data pipelinesWhat Success Looks Like:Reliable, scalable pipelines for marketing data ingestionHigh-quality datasets enabling accurate campaign and lead analysisStrong partnership with marketing teams, translating business needs into data solutionsImproved visibility into lead quality, attribution and campaign performanceClear ownership of end-to-end data use cases, not just components

📌 Senior Data Engineer (Madhya Pradesh)
🏢 HappieHire
📍 Madhya Pradesh