Senior Data Engineer

We’re seeking a Senior Data Engineer to lead the technical direction and architecture of the Data Foundation Platform within OneSuite. This is a hands-on engineering role - you’ll contribute directly to production code, build pipelines, and review implementations while shaping the long-term data platform architecture. You’ll operate as the technical authority for DFP - guiding design patterns, ensuring code quality, and solving complex problems in distributed data systems. You’ll collaborate closely with peer charters to ensure the data foundation remains reliable, scalable, and production-ready. Data Platform Architecture

• Lead the hands-on design, coding, and evolution of DFP’s cloud-native data architecture (AWS/GCP/Snowflake).
• Architect and implement scalable ingestion and transformation pipelines using Fivetran, Databricks, and Airflow/DBT.
• Design and code modular data services and APIs that unify campaign, conversion, and audience datasets across 20+ marketing platforms.
• Build and optimize schemas, transformations, and ETL logic for high-performance, reusable data workflows.
• Contribute directly to the DFP codebase (Python, SQL, Spark) - developing pipelines and infrastructure alongside the engineering team.
• Establish data versioning, testing, and governance frameworks ensuring reliability, lineage, and compliance.

Data Engineering Leadership

• Drive best practices in data modeling, CI/CD, code reviews, and performance optimization.
• Collaborate across engineering teams to define data contracts, schema validation rules, and automated quality checks.
• Mentor junior engineers by pairing on code, reviewing pull requests, and helping them deepen technical maturity.
• Participate actively in sprint planning and retrospectives, ensuring engineering execution aligns with platform goals.
• Partner with Product and Data Science teams to translate analytical requirements into efficient, production-grade data solutions.

AI-Ready Infrastructure

• Design and maintain pipelines supporting Retrieval-Augmented Generation (RAG) and Context Engine workflows.
• Build high-performance APIs and caching strategies enabling low-latency data access for AI agents and orchestration systems.
• Implement vector-based data retrieval layers (pgVector, Pinecone) and ensure efficient embedding pipelines for AI contexts.
• Partner with AI teams to monitor data latency, cost efficiency, and observability metrics.

Collaboration & Governance

• Partner with Platform Operations and Security to enforce privacy, compliance, and access control frameworks (GDPR, SOC2).
• Work cross-functionally with analysts, AI engineers, and platform leads to deliver production-grade, business-critical data products.
• Lead by example in data documentation, pipeline testing, and lineage tracking - ensuring transparency and reproducibility across all pipelines.

• Hands-on experience as a Data Engineer or Data Platform Developer, including direct contributions to production codebases.
• Expert-level proficiency in Python, SQL, and Apache Spark for data pipeline development.
• Proven experience building ELT workflows using Fivetran, Airflow, DBT, or Databricks.
• Solid understanding of data modeling, dimensional design, and schema normalization at enterprise scale.
• Strong experience with AWS (S3, Redshift, Glue, Lambda) or GCP (BigQuery, Dataflow).
• Experience working with marketing or ad platform data (Google Ads, Meta, TikTok, DV360, Amazon Ads).
• Demonstrated expertise in code versioning (GitHub), CI/CD integration, and data observability practices.
• Ability to write clean, modular, testable code and review peers’ contributions for maintainability and performance.