Data Engineer

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.

The candidate will have responsibilities across the following functions:

Data Engineering and Pipeline Development:

• Design, build, and maintain robust data pipelines for marketing, social advertising, product, CRM, and customer data sources.
• Develop scalable ETL/ELT workflows and Python/SQL-based data processing solutions.
• Integrate data from APIs, event streams, batch systems, databases, and third-party marketing platforms.
• Build reusable data ingestion frameworks and microservices to support growing marketing data requirements.
• Ensure high standards of data quality, reliability, scalability, and pipeline observability.
• Optimize data models and transformation layers for analytics, reporting, and downstream machine learning use cases.
• Implement automated data validation, monitoring, alerting, and failure-recovery mechanisms.

Marketing and Ad Platform Data:

• Own the ingestion, transformation, and modeling of data from Meta Ads (Facebook), Google Ads, and other digital marketing platforms.
• Build scalable datasets supporting campaign performance, acquisition, lead generation, funnel analysis, attribution, and conversion tracking.
• Develop campaign-level, ad-set-level, and ad-level datasets to analyze marketing performance.
• Track and analyze metrics including impressions, clicks, CTR, CPC, CPM, conversions, CPA, ROAS, spend, and conversion rate.
• Understand digital advertising concepts, including campaign/ad set/ad hierarchy, bidding and optimization signals, attribution windows, pixel/event tracking, and conversion events.
• Integrate advertising-platform data with CRM and customer datasets to evaluate lead quality, downstream conversion, revenue contribution, and customer acquisition efficiency.

Marketing Analytics and Conversion Optimization:

• Perform campaign and conversion analytics to understand the complete customer journey from acquisition through lead generation and final conversion.
• Build marketing funnel datasets to identify conversion drop-offs, high-performing channels, audiences, campaigns, and creatives.
• Analyze Meta Ads and Google Ads performance to identify opportunities for improving acquisition efficiency and conversion rates.
• Develop analytics solutions for lead scoring, lead quality measurement, customer segmentation, and campaign effectiveness.
• Support attribution analysis by connecting marketing touchpoints with leads, conversions, and revenue outcomes.
• Enable data-driven decisions around audience targeting, campaign performance, channel efficiency, and marketing ROI.
• Partner with marketing and growth teams to define KPIs and success metrics tied to revenue, lead quality, acquisition cost, and conversion efficiency.

Business Understanding and Cross-Functional Collaboration:

• Translate requirements from Marketing, Growth, Product, Analytics, and Business teams into scalable data engineering solutions.
• Work closely with Data Leads and Engineering Leads to define architecture, data models, technical approaches, and implementation strategies.
• Develop trusted datasets that enable self-service marketing analytics and business intelligence.
• Communicate complex data and engineering concepts clearly to technical and non-technical stakeholders.
• Understand business objectives and ensure that data products directly support measurable business outcomes.

Data Quality, Governance, and Observability:

• Implement comprehensive data quality checks, validation frameworks, monitoring, and alerting across marketing data pipelines.
• Ensure consistency and reconciliation across Meta Ads, Google Ads, CRM, product, and internal data sources.
• Monitor data freshness, completeness, accuracy, and pipeline SLA adherence.
• Establish clear documentation for data models, pipelines, source systems, transformations, and business definitions.
• Build reliable and governed datasets that can be trusted for executive reporting, campaign optimization, and business decision-making.
• Built and maintained data pipelines to ingest and transform Meta Ads, Google Ads, CRM, and campaign performance data for marketing analytics and reporting.
• Developed analytics datasets for campaign, ad-set, and ad-level performance tracking, enabling detailed analysis of spend, impressions, clicks, CTR, CPC, conversions, CPA, and ROAS

Requirements:

• Strong experience in Data Engineering using Python and SQL.
• Hands-on experience building scalable ETL/ELT and API-based data ingestion pipelines.
• Experience working with marketing/ad-platform data, particularly Meta Ads.
• Strong understanding of campaign analytics, conversion funnels, attribution, lead generation, and marketing KPIs.
• Ability to connect technical data solutions with revenue, lead quality, customer acquisition, and conversion outcomes.
• Strong analytical and problem-solving skills with the ability to work across engineering, data, marketing, and growth teams.
• Experience building reliable, well-documented, and self-service-ready analytical datasets.

Skills

Databricks, Google Ads, Google Analytics, Meta Ads, python