Head of Data Engineering and Analytics

Must have skills required :

DBT, GCP, DWH, Data Modelling, Data Governance, data quality, data monitoring, Cost Management

Good to have skills :

Multi Cloud

Forbes Advisor (One of Uplers' Clients) is Looking for :

Senior Data Architect who is passionate about their work, eager to learn and grow, and who is committed to delivering exceptional results. If you are a team player, with a positive attitude and a desire to make a difference, then we want to hear from you.

Role Overview Description

Company Description

Forbes Advisor, part of the Forbes Marketplace family, provides consumers with

expert-written insights, news, and reviews on personal finance, health, business, and

everyday life decisions. We empower our audience with data-driven knowledge so

they can make informed choices confidentlybalancing the agility of a startup with

the stability of a seasoned enterprise

Role Overview

The Senior Data Architect is a strategic, senior leadership role responsible for

setting the vision and direction of our data warehousing function. You will architect,

implement, and maintain a state-of-the-art data warehouse that drives actionable

insights across revenue, subscriptions, paid marketing channels, and operational

functions. Your leadership will ensure data quality, robust pipeline design, and

seamless integration with business intelligence tools. This role requires a strong mix

of technical acumen, team management, and cross-functional collaboration

especially with teams focused on SEM, Digital Experiences, and revenue attribution

Job Description

Key Responsibilities

Strategic Data Architecture & Pipeline Leadership

Vision & Strategy :

Define and execute the long-term strategy for our data warehousing

platform using medallion architecture (Bronze, Silver, Gold layers) and

modern cloud-based solutions.

End-to-End Pipeline Oversight :

Oversee data ingestion (via Google Ads, Bing Ads, Facebook Ads, GA,

APIs, SFTP, etc.), transformation (leveraging DBT, and SQL [via

BigQuery]), and reporting, ensuring that our pipelines are robust and

scalable.

Data Modeling Best Practices :

Champion best practices in data modeling, including the effective use

of DBT packages to streamline complex transformations.

Data Quality, Governance & Attribution

Quality & Validation :

Establish and enforce rigorous data quality standards, governance

policies, and automated validation frameworks across all data streams.

Standardization & Visibility :

Collaborate with the Data Engineering, Insights and BIOps team to

standardize data definitions (including engagement metrics and

revenue attribution) and ensure consistency across all reports.

Attribution Focus :

Develop frameworks to reconcile revenue discrepancies and unify

validation across Finance, SEM, and Analytics teams.

Ensure accurate attribution of revenue and paid marketing channel

performance, working closely with SEM and Digital Experiences teams.

Monitoring & Alerting :

Implement robust monitoring and alerting systems (e.g., Slack and

email notifications) to quickly identify, diagnose, and resolve data

pipeline issues.

Team Leadership & Cross-Functional Collaboration

People & Process :

Lead, mentor, and grow a high-performing team of data warehousing

specialists, fostering a culture of accountability, innovation, and

continuous improvement.

Stakeholder Engagement :

Partner with RevOps, Analytics, SEM, Finance, and Product teams to align

the data infrastructure with business objectives.

Serve as the primary data warehouse expert in discussions around

revenue attribution and paid marketing channel performance, ensuring

that business requirements drive technical solutions.

Communication :

Translate complex technical concepts into clear business insights for

both technical and non-technical stakeholders.

Operational Excellence & Process Improvement

Deployment & QA :

Oversee deployment processes, including staging, QA, and rollback

strategies, to ensure minimal disruption during updates.

Continuous Optimization :

Regularly assess and optimize data pipelines for performance,

scalability, and reliability while reducing operational overhead.

Legacy to Cloud Transition :

Lead initiatives to transition from legacy on-premise systems to

modern cloud-based architectures for improved agility and cost

efficiency.

Innovation & Thought Leadership

Emerging Trends :

Stay abreast of emerging trends and technologies in data warehousing,

analytics, and cloud solutions.

Pilot Projects :

Propose and lead innovative projects to enhance our data capabilities,

with a particular focus on predictive and prescriptive analytics.

Executive Representation :

Represent the data warehousing function in senior leadership

discussions and strategic planning sessions

Qualifications

Education & Experience

• Bachelors or Masters degree in Computer Science, Data Science, Information
• Systems, or a related field.
• 15+ years of experience in data engineering, warehousing, or analytics roles,
• with at least 5+ years in a leadership capacity.
• Proven track record in designing and implementing scalable data
• warehousing solutions in cloud environments.

Technical Expertise

Skills Required

Analytics, Data Modelling, Data Processing, Databricks, Sql, Communication Skills