Data Engineer L3 in Bengaluru, Karnātaka at Marketplace Operations Inc.

Forbes Advisor is a new initiative for consumers under the Forbes Marketplace umbrella that provides journalist- and expert-written insights, news and reviews on all things personal finance, health, business, and everyday life decisions. We do this by providing consumers with the knowledge and research they need to make informed decisions they can feel confident in, so they can get back to doing the things they care about most.

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, high-quality 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 upon

The ideal candidate will not manage campaign buying or bidding directly but must clearly understand ad platform mechanics, attribution models and lead quality scores and will work closely with the Data Lead / Engineering Lead, acting as a key contributor in shaping solutions, making technical decisions, and delivering high-impact data products..

Responsibilities:

1. 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 cases

2. Marketing & Ad Platform Data

Own ingestion and modeling of data from Meta Ads (Facebook) and other digital marketing platformsBuild datasets that support:Campaign performance trackingLead funnel analysisAttribution and conversion trackingUnderstand key concepts such asCampaign structure (campaign/ad set/ad level)Bidding & optimization signalsAttribution windowsPixel / event tracking

3. Business 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 datasets

4. Data Quality & Governance

Implement validation checks, monitoring and alerting for pipelinesEnsure consistency across different marketing data sourcesMaintain clear documentation of data models and pipelines

5. Business 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 performance

•Own key use cases such as:

➢Lead funnel optimization

➢Campaign attribution

➢Revenue reporting and forecasting

•Ensure data enables decision-making, not just reporting

6. Engineering 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 modularity

▪Pipeline reliability and monitoring

▪Documentation and data contracts

Contribute to shared frameworks and reusable componentsPromote best practices across the data engineering team

Required Skills & Qualifications

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 optimization

2.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 metrics

III. Ability to interpret marketing data beyond surface-level metrics

IV. Exposure to event tracking systems (GA4, Snowplow, etc)

Good 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 pipelines

What 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

Why Join Us

Work at the intersection of data engineering and growth marketingSolve high-impact problems in performance marketing and attributionOwn meaningful data products end-to-endInfluence both technical architecture and business outcomesBe part of a team that values ownership, impact and engineering excellence

Perks:

● Day off on the 3rd Friday of every month (one long weekend each month)

● Monthly Wellness Reimbursement Program to promote health well-being

● Monthly Office Commutation Reimbursement Program

● Paid paternity and maternity leaves