Role: Data Engineer L3
Experience: 7+ years
Work-mode: Remote
Employement type: Full time

Budget: 38 LPA

Role 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 architecture
- Driving 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:

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 SQL
Ensure high data quality, reliability and observability across pipelines

Optimize data models for analytics and reporting use cases

Marketing & Ad Platform Data:

1. Own ingestion and modeling of data from Meta Ads (Facebook) and other digital

marketing platforms

1. Build datasets that support:

Campaign performance tracking
Lead funnel analysis
Attribution and conversion tracking

1. Understand key concepts such as:





Campaign structure (campaign/ad set/ad level)
Bidding & optimization signals
Attribution windows

Pixel / event tracking

Business Understanding & Collaboration: Translate business requirements from marketing, growth and product teams into scalable data solutions

Define success metrics tied to revenue and performance

Enable self-serve analytics through well-structured datasets

Data Quality & Governance: Implement validation checks, monitoring and alerting for pipelines

Ensure consistency across different marketing data sources

Maintain clear documentation of data models and pipelines

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

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 components

Promote best practices across the data engineering team

Required Skills & Qualifications:

1. Core Technical Skills

Strong proficiency in Python (must-have)
Advanced SQL skills for large-scale data processing
Hands-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 (Big Query, etc.)
Experience building scalable data ingestion systems
Familiarity with microservices-style or modular data systems
Strong understanding of performance and cost optimization

1. Ad Platform Knowledge:

Solid understanding of Meta Ads platform fundamentals

Familiarity with:

- Campaign hierarchy and metrics (CTR, CPC, CPA, ROAS)
- Conversion tracking and attribution models
- Lead generation workflows and funnel metrics

Ability to interpret marketing data beyond surface-level metrics

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 ingestion

High-quality datasets enabling accurate campaign and lead analysis
Strong partnership with marketing teams, translating business needs into data solutions
Improved visibility into lead quality, attribution and campaign performance

Clear ownership of end-to-end data use cases, not just components

📌 Senior Data Engineer (Indore)
🏢 HappieHire
📍 Indore