What are the responsibilities and job description for the Sr Data Engineer position at datafuelX Inc.?
About the Role
We're looking for a Senior Data Engineer that is innovative, curious, and collaborative to help
evolve the data platform that powers our sell-side media business. This role supports
data-driven workflows and a broad range of media monetization use cases, with an emphasis
on scalable, open data architectures.
You'll work on high-volume, revenue-critical media data while helping modernize how data is
stored, processed, and served across the organization. This is a hands-on role for engineers
who enjoy building, migrating, and improving platforms, not just maintaining them.
What You'll Do
- Design, build, and maintain scalable, reliable data pipelines that support sell-side media
monetization. - Ingest, integrate, transform, and model high-volume media and campaign data from
multiple sources, delivering analytics-ready datasets that meet quality, accessibility, and
business requirements. - Develop lakehouse-style data models that balance flexibility, performance, and cost
efficiency, enabling reporting, analytics, operational workflows, and downstream
consumption. - Build, optimize, and maintain ETL/ELT processes for both batch and near-real-time
workloads, orchestrated with modern workflow tools (e.g., Airflow, Dagster). - Collaborate with machine learning, client-facing, product, application, and analytics teams to understand requirements, support ML feature productionization, and enable
self-service insights. - Ensure data quality, lineage, observability, governance, and security, safeguarding cloud
data assets and maintaining trust in revenue-critical systems. - Contribute to platform evolution and migration efforts, including evaluating tools,
improving workflows, and reducing architectural complexity. - Continuously optimize pipelines, queries, and storage for performance, scalability, and
cost efficiency. - Mentor junior engineers and participate in technical design and architecture reviews.
- Detect, investigate, and resolve data anomalies to maintain pipeline reliability and data
trustworthiness.
Required Experience & Skills
Core Requirements
- 5 years of experience in building highly scalable and reliable data engineering and
analytics platforms - Strong experience building and optimizing modern data pipelines, architectures, and
datasets using Data Lake, Data Warehouse, and Lakehouse paradigms - Advanced SQL, Python, and PySpark skills and experience building analytics-ready data models
- Experience using Iceberg, Delta, and Parquet data formats
- Experience using Dagster and Airflow orchestration tools
- 2 years hands-on experience with cloud platforms, such as GCP & AWS
- Experience designing and building data pipelines on object storage-based platforms (e.g., S3, Wasabi, Dremio, Snowflake, Delta Lake) to process and manage large-scale analytical datasets
- Solid understanding of data quality, testing, monitoring, and operational reliability
- Strong communication skills and ability to work closely with technical and non-technical
stakeholders - Experience collaborating with Software Engineers using Agile methodologies to build web applications that access, visualize, and sometimes update big data stores in a hybrid OLAP and OLTP environment.
- Bachelor's degree or equivalent work experience (minimum 5 years) in Computer Science or related field
Preferred/Nice-to-Have
- Experience in media industry strongly preferred including familiarity with sell-side
concepts (linear TV or digital inventory, campaign delivery, pacing, audience-based selling and measurement) - 1 years experience supporting or leading data platform migrations, hybrid architectures, or warehouse-to-lakehouse transitions using Dremio is a strong plus
- Experience with Tableau, Metabase, or other data visualization tools
- Experience working in an operational environment with time-sensitive customer
commitments