Data Engineering Manager (Remote)

Experience: 8.00 + years

Confidential (based on experience)

Shift : (GMT+05:30) Asia/Kolkata (IST)

Opportunity Type : Remote

Placement Type : Full time Permanent Position

(*Google Cloud, Google Ads, Meta, Taboola, GA4, Machine Learning

Forbes Digital Marketing Inc. is a high-growth digital media and technology company dedicated to helping consumers make confident, informed decisions about their money, health, and everyday life.

We combine data-driven content, rigorous experimentation, and modern engineering to power a portfolio of global products and partnerships.

Our Data & Analytics organisation builds the infrastructure and intelligence that drive every marketing, product, and revenue decision. We're now hiring a Data Engineering Manager to lead key squads delivering the next generation of data products across revenue attribution, partner ingestion, and first-party data pipelines.

As a Data Engineering Manager, you'll lead a team of engineers responsible for the design, delivery, and reliability of our data platform.

You will guide a squad of 10–15 engineers (3–4 direct reports) in building scalable, observable, and well-modeled data systems that power marketing, revenue, and analytics decisions across the company.

This role combines technical depth with delivery leadership: you'll drive design and best practices, mentor engineers, and partner with Program Management to ensure predictable, high-quality releases. You'll collaborate across Analytics, RevOps, Data Ops, and Product to turn business problems into performant, future-ready engineering solutions.

Commercial Impact

You will be crucial to driving revenue growth for the business through flexible and scalable data ingestion, creation of microservices that can be used for decisioning and driving forward data practices.

Lead, mentor, and develop a high-performing data engineering squads delivering production-grade pipelines and services.

Set technical and operational standards for quality, documentation, and reliability.

Foster a culture of ownership, engineering excellence, and continuous improvement.

Contribute to architectural reviews and solution design in collaboration with the Director of Data Engineering.

Ensure scalable, modular pipeline design and adherence to strong data modeling principles in dbt and BigQuery.

Lead the transition from monolithic pipelines to microservice-based data workflows that are reusable and observable.

Champion consistency and semantic alignment across datasets and layers (Bronze, Silver, Gold).

Oversee implementation and optimisation of workflows in GCP (BigQuery, Cloud Composer, Cloud Functions, Vertex AI etc.).

Drive observability, cost efficiency, and data quality through monitoring and alerting standards.

Evaluate new tools and frameworks that enhance scalability and engineering productivity.

Partner closely with Analytics, BI Ops, and RevOps to deliver reliable data foundations for attribution, 1PD activation, and performance optimisation.

Communicate progress, risks, and dependencies clearly to technical and non-technical partners.

Embed best practices for documentation, schema governance, and dependency management.

Model engineering-first leadership — balancing technical depth with empathy, clarity, and decisiveness.

10+ years of total experience in data or software engineering, including 2+ years leading teams.

Deep expertise in SQL, Python, and modern ELT tools (dbt, Airflow/Composer).

Strong understanding of data modeling, orchestration, and optimisation in GCP (BigQuery, GCS, Pub/Sub).

Demonstrated experience building or scaling data pipelines for marketing, attribution, or monetisation.

Proven ability to lead delivery within agile sprints and coordinate with cross-functional stakeholders.

Experience transitioning large-scale architectures to microservices or event-driven designs.

Familiarity with CI/CD pipelines, observability frameworks, and cost optimisation in cloud environments.

Exposure to machine-learning pipelines, model serving, or real-time scoring.

Background working with marketing or product analytics data sources (Google Ads, Meta, GA4, Taboola, etc.).

Fully operational, microservice-based data workflows with strong observability and documentation.

Consistent, semantic data models powering analytics and attribution across the business.

A self-sufficient, empowered engineering team operating with speed, clarity, and quality.

Data platform seen as a reliable, scalable foundation for 1PD strategy and future AI use cases.

Remote-first, collaborative culture with flexible working hours.

Opportunity to shape the engineering foundations of a world-class data organisation.

Monthly long weekends, wellness stipend, and generous parental leave.

Work with global leaders in data, marketing, and engineering — building systems that drive real business growth.

Step 3: Increase your chances to get shortlisted & meet the client for the Interview!

Our role will be to help all our talents find and apply for relevant contractual onsite opportunities and progress in their career. So, if you are ready for a new challenge, a great work environment, and an opportunity to take your career to the next level, don't hesitate to apply today.