Description: Location: US-Remote (AL,AZ,CA,CO,CT,DC,FL,GA,ID,IL,IN,MA,MD,MI,MO,NC,NH,NJ,NV,NY,OH,PA,RI,TN,TX,UT,VA,WA,WI) Type: Full-Time, Salary, Exempt About DMS Digital Media Solutions (DMS) is a performance-driven digital marketing company that connects consumers and brands through data, technology, and proprietary media solutions. Operating in high-volume, transaction-intensive markets, DMS manages multiple revenue streams, variable margins, and complex unit economics across its portfolio of offerings. The company partners closely with clients to deliver measurable outcomes, leveraging analytics, optimization, and disciplined execution in dynamic market conditions. About this Role We’re looking for a Manager, Data Engineering to lead the team responsible for the reliability, scalability, and performance of our data infrastructure. You will manage a team of data engineers and be accountable for the platforms and pipelines that power DMS’s data-driven business. This role owns the strategy and execution across high-availability transactional systems (MySQL, PostgreSQL, DynamoDB), ELT/ETL pipelines, Kafka streaming architectures, replication systems, and data warehousing infrastructure. You will partner closely with core engineering, data science, product, and infrastructure teams to ensure safe, scalable, and cost-efficient data movement across the organization. Why Join DMS? At Digital Media Solutions, you’ll work at the core of a high-scale marketing marketplace that processes millions of consumer interactions and signals daily. Your work will directly impact how traffic is evaluated, matched, and monetized—giving you clear visibility into how data drives revenue and performance. In this role, you won’t just report on data—you’ll shape it. You’ll have the opportunity to influence lead quality, improve buyer matching, and optimize campaign outcomes in real time. You’ll work with modern analytics tools and large datasets, gaining hands-on experience in a fast-paced environment where insights quickly turn into action. If you enjoy solving complex problems, working with high-volume data, and seeing the direct impact of your work on business performance, this is an opportunity to make a meaningful impact. Requirements: What You’ll Do

• Lead, mentor, and grow a team of data engineers and architects
• Define and execute the technical roadmap for production database systems (MySQL, PostgreSQL, DynamoDB, Elastic)
• Own the architecture and governance of binlog replication, logical replication, and CDC workflows
• Drive strategy and reliability for ELT/ETL pipelines and Kafka-based streaming architectures
• Set standards for performance optimization, query tuning, indexing, and database scaling across teams
• Oversee backup, failover, disaster recovery (PITR), and incident response for all production data systems
• Drive cost efficiency, infrastructure optimization, and monitoring across cloud-managed data services (AWS RDS, Aurora, DynamoDB)
• Champion data integrity, security, and compliance standards across all data engineering work
• Partner cross-functionally with backend, data science, infrastructure, and product teams to align on data platform priorities
• Establish engineering guardrails, best practices, and documentation to enable team autonomy and quality at scale
• Lead the evaluation and selection of next-generation data warehousing technology (Snowflake, Databricks, AWS Redshift Serverless) — assessing performance, cost, ecosystem fit, and migration complexity to inform a platform decision
• Own the design of an upgraded data model for the warehouse in partnership with data engineers and architects, establishing standards for schema design, partitioning, access patterns, and downstream consumption
• Oversee the end-to-end migration from the current Redshift warehouse — planning the phased approach, managing cutover risk, and ensuring continuity of downstream reporting and analytics throughout

Technical Environment

• Databases: MySQL, PostgreSQL, DynamoDB, Elastic, Redis
• Streaming: Kafka (CDC, event streaming)
• Cloud: AWS (RDS, Aurora, DynamoDB)
• Data Warehouse: AWS Redshift, Snowflake, Databricks
• Monitoring: Datadog, CloudWatch
• Tooling: Go, Python, Bash
• Data Movement: ELT / ETL pipelines

What We’re Looking For

• 8+ years of data or database engineering experience, with 2+ years in an engineering management role
• Deep hands-on experience with MySQL and/or PostgreSQL in high-availability production environments
• Proven track record leading and developing teams of engineers in fast-paced, data-intensive environments
• Strong expertise in binlog/logical replication and integrating OLTP systems with Kafka
• Experience designing and supporting ELT/ETL pipelines safely at scale
• Deep understanding of ACID principles, transaction isolation, and database internals
• Experience with AWS-managed database services (RDS, Aurora, DynamoDB) and infrastructure cost management
• Experience evaluating or migrating data warehouse platforms (Redshift, Snowflake, Databricks, or similar)
• Experience designing or redesigning dimensional or analytical data models for large-scale warehouse environments
• OLTP vs OLAP workload separation and architectural decision-making experience
• Ability to balance performance, cost, and compliance at an organizational level
• Strong communication skills with the ability to translate technical complexity for non-technical stakeholders

Nice to Have

• Experience with CDC implementation and large-scale database migrations
• Infrastructure-as-code experience (Terraform)
• Background in high-volume, transaction-intensive production environments
• Prior experience building or scaling a data engineering function from the ground up

Expectations at Manager Level

• Owns team health, technical direction, and delivery outcomes for the data engineering function
• Designs and communicates safe, scalable replication and streaming architectures to senior leadership
• Protects transactional systems from downstream risk at an organizational level
• Anticipates scaling and cost challenges and proactively drives solutions across the team
• Establishes guardrails, engineering standards, and a culture of ownership across data engineering teams

COMPENSATION: The anticipated annual base salary for this position is $180,000–$200,000. Actual compensation may vary based on work experience, education, and skill level.