Senior Manager, Data Engineering

Role Overview

• Lead Data Platform Architecture and Delivery
• Own the technical direction and delivery of cloud-native data platforms built on Databricks and AWS
• Lead the design and implementation of Lakehouse architecture — Delta Lake, Unity Catalog, Spark-based processing — across client engagements
• Ensure scalability, performance, security, and cost optimization are built into solutions from the start, not addressed after delivery
• Build and Optimize Data Pipelines
• Oversee ingestion, transformation, and orchestration pipelines across both batch and streaming workloads
• Apply AWS-native services (Glue, Step Functions, Lambda, Redshift, S3) alongside Databricks to build reliable, production-grade pipelines
• Establish reusable pipeline patterns and delivery accelerators that raise consistency across the team
• Enable AI, ML, and GenAI Use Cases
• Ensure data is accessible, trusted, and production-ready to support model development and deployment across AI and GenAI initiatives
• Collaborate closely with data science and analytics teams to align platform capabilities with active and planned AI programs
• Contribute to client AI roadmap conversations by translating platform decisions into clear enablement strategies
• Engage Clients on Technical Strategy
• Participate in client-facing architecture reviews and technical strategy sessions, communicating complex platform concepts as practical business outcomes
• Support senior leadership in executive briefings and help clients make confident decisions about platform direction and investment
• Partner with strategy and delivery teams to scope and structure data platform engagements
• Develop the Data Platform Team
• Lead, mentor, and grow a team of data engineers, architects, and platform specialists
• Set technical standards and create an environment where craft and continuous improvement are expected
• Identify skill gaps and help build the team's capabilities alongside the growth of OneMagnify's data practice

Requirements

• 7+ years of experience in data engineering, data architecture, or analytics, with at least 2 years in a team leadership role
• Proven hands-on experience with Databricks in production environments (Delta Lake, Unity Catalog, Spark)
• Deep experience with AWS data services — Glue, Redshift, S3, Lambda, Step Functions — in production
• Strong expertise in modern data patterns: Lakehouse architecture, ELT/ETL, streaming, APIs, and pipeline orchestration
• Solid understanding of AI/ML and GenAI ecosystems, including data requirements for model development and deployment
• Ability to lead technical teams, communicate clearly with client stakeholders, and drive delivery quality across engagements

Tech Stack

• Amazon Redshift
• AWS
• Cloud
• ETL
• Spark
• Unity

Benefits

• medical, dental, and vision coverage
• 401(k) retirement plan
• paid holidays
• Flexible Time Off (FTO)
• additional programs focused on wellness, financial security, and professional growth