Senior Manager, Data Engineering

NinjaHoldings•Chicago, IL

1d•$135,000 - $175,000•Hybrid

About The Position

This position will lead the team responsible for the data platforms and pipelines that power reporting, analytics, operational decision making, and future AI capabilities across the company. This is a technical leadership role focused on setting direction, developing the team, and making sure our data platform continues to evolve with the needs of the business. The person in this role should have enough technical depth to step in when needed, participate in architecture discussions, review designs, and help solve difficult problems. Our current stack includes Snowflake, dbt, Fivetran, Meltano, PostgreSQL, and AWS. We are also modernizing our warehouse architecture around a Medallion model and building a stronger foundation for analytics and AI. We are looking for someone who sees Data Engineering as a platform capability for the entire company, not simply a team that maintains pipelines and supports reporting. The right person will think beyond today's reporting requirements, build reliable systems today, and make architectural decisions that give us more options tomorrow. We want someone who can help us build a data platform that will support the company several years from now, including larger data volumes, more advanced analytics, automation, and AI powered applications. That means making data easier to trust and use, improving the effectiveness of the team, and keeping the platform scalable and cost effective.

Requirements

6+ years of experience in Data Engineering, Data Platform Engineering, Database Engineering, or a related field
2+ years of experience leading or managing Data Engineers or other technical teams
Strong experience with Snowflake, dbt, SQL, and modern ELT or ETL architectures
Strong understanding of PostgreSQL or similar relational databases, including schema design, indexing, query performance, and reliability
Experience with data ingestion and integration tools such as Fivetran, Meltano, or similar technologies
Experience with dimensional modeling, Medallion architecture, or other modern warehouse design patterns
Strong understanding of data quality, automated testing, observability, lineage, governance, and security
Experience operating cloud based data platforms and understanding performance and cost tradeoffs
Ability to think at both the architecture level and the implementation level when needed
Strong communication skills and the ability to work effectively across Engineering, Analytics, business teams, and leadership

Nice To Haves

Experience with Python or Ruby for automation, tooling, or data processing
Experience in financial services or another regulated industry
Experience supporting analytics or AI applications
Experience with data catalogs, data contracts, or master data management
Experience leading a significant warehouse or data platform modernization effort
Experience using AI and automation to improve engineering productivity, monitoring, documentation, or data quality

Responsibilities

Lead the Data Engineering team and set the technical direction for our data platform
Own the architecture, reliability, performance, and evolution of Snowflake, PostgreSQL, and the pipelines that move data across our systems
Lead our transition to a Medallion architecture and establish clear standards for how data is ingested, transformed, modeled, tested, documented, and consumed
Own and evolve our modern data stack, including Snowflake, dbt, Fivetran, Meltano, PostgreSQL, AWS, and related technologies
Partner with application engineering teams on transactional database design, schema changes, performance, scalability, and reliability
Work with Engineering, Analytics, Product, Finance, Risk, Marketing, and other business teams to turn data needs into reusable, well designed solutions instead of one off reporting work
Establish strong engineering practices around data quality, automated testing, observability, lineage, security, documentation, and incident response
Improve self service access to data so teams can safely understand and use data without relying on Data Engineering for every request
Monitor and optimize the platform for performance, scalability, and cost
Build a long term roadmap that balances current business needs with the future of the platform, including AI use cases
Lead, mentor, and develop a strong team of Data Engineers
Set clear technical standards and expectations while giving engineers ownership of their work
Participate in architecture and design decisions, challenge assumptions, and step in technically when needed
Help the team prioritize work based on business impact, technical risk, reliability, and long term value
Create a culture where data quality, testing, documentation, and operational reliability are treated as part of the product
Communicate technical decisions, tradeoffs, risks, and investments clearly to both technical and nontechnical teams