Senior Data Engineer

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
2. Build datasets that support:
Campaign performance tracking
Lead funnel analysis
Attribution and conversion tracking
3. 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 (BigQuery, etc.)
Experience building scalable data ingestion systems
Familiarity with microservices-style or modular data systems
Strong understanding of performance and cost optimization
2. 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

Work arrangement
No