About this role
Job DetailsWe’re hiring a Engineering Manager, Data Engineering to drive our Data Engineering team’s people, projects, and processes forward. At Openly, our growth has put an emphasis on building and maintaining a robust, scalable data platform to ensure teams can access high-quality data efficiently. Our data engineering function is responsible for designing and maintaining data pipelines, data architectures, and data infrastructure while working closely with data science, business intelligence, and product teams to enable data-driven decision-making across the organization.
As the Engineering Manager of the Data Engineering Team, you will provide technical leadership and influence decisions about data architecture, data pipeline strategies, and technical processes. You will drive our data engineering culture, manage and mentor a diverse group of data engineers, and work strategically across teams to ensure our data platform evolves with business needs. While this is primarily a leadership role, this position will also have the capacity to pick up technical work during periods of high demand, demonstrating technical credibility with the team and staying connected to the evolution of our data systems.
Key ResponsibilitiesManage, support, and mentor a diverse group of data engineers
Partner with the Principal Engineers and engineering leadership to guide the data architecture, scalability, and evolution of our data platform
Work closely with data science, business intelligence, and product teams to align data solutions with business objectives, balancing technical investments with business priorities
Identify, resolve, or escalate roadblocks and risks that threaten team deliverables and goals, seeking additional context or direction when needed
Drive each team member toward their career goals through clear milestones, transparent feedback, and a culture of open communication that sets them up for success
Translating data concepts and requirements into architecture decisions and technical implementation strategies
Provide technical input on data pipeline design, data modeling, and technology selections
Contribute hands-on technical work during peak periods to support the team and maintain technical credibility
Establish best practices for data pipeline architecture, data quality, and performance optimization
Runs cross functional working groups to deliver on larger company objectives
Participate in domain standups, weekly 1:1s, team collaborations, biweekly demos, and biweekly retros
Share your knowledge within the data engineering team and with others in the company (e.g., engineering all-hands, engineering learning hour, domain meetings)
Partner closely with the management team, HR, and Recruiting to attract, recruit, and develop a diverse team of high-quality data engineers
Our StackBackend/Core: Go & PostgreSQL
Frontend: Browser-based, VueJS, Webpack, Nuxt & Tailwind
Research/Data Science: R, VertexAI, Hex, & Python
Data: GCP GCS, BigQuery, Composer/Airflow, Cloud Functions, PostgreSQL, SQL, Python, Aiven Debezium and Kafka, Fivetran
Infrastructure: Google Cloud (Cloud Run, Kubernetes, Pub/Sub, BigQuery, CloudSQL), managed with Terraform. GitHub for code hosting, DataDog for monitoring, CircleCI for CI/CD pipelines Remote work tools: Slack, Zoom
RequirementsIf you don’t think you meet all of the criteria below but still are interested in the job, please apply. Nobody checks every box, and we’re looking for someone excited to join the team.
5+ years of data engineering and data platform development experience, including 3+ years of engineering management or technical leadership in a data/infrastructure/operations environment
Deep experience with data infrastructure components, including data lakes and lakehouses (e.g., Iceberg), data warehouses (e.g., Snowflake, BigQuery), online and offline data stores, and both batch and real-time streaming systems
Proven, hands-on expertise designing and maintaining scalable, secure, and cost-efficient data platforms and pipeline architectures — including schema design, data modeling, partitioning, data replication, staging, transformation, and movement — aligned to business goals and objectives
Comfortable working with modern data and infrastructure technologies, such as Spark, Flink, Kafka, Airflow, Kubernetes, and similar tools
Experience with Google Cloud data technologies (BigQuery, Cloud Composer, GCS, etc.) or similar cloud data platforms
Infrastructure as Code (IaC) experience with Terraform to define, manage, and version cloud data infrastructure
Proficiency in Python or similar languages (e.g., Java, Scala), with strong SQL skills and performance tuning for analytical workloads
Understanding of data governance, security, and compliance best practices (e.g., RBAC, PII handling, auditability), with experience designing systems that meet regulatory and internal standards
Demonstrated ability to understand data requirements, translate them into source-to-target data mappings, and build scalable solutions
Experience guiding and mentoring a diverse group of engineers while balancing their growth with business requirements
Highly autonomous with the ability to prioritize and work through ambiguity
Ability to effectively communicate technical and strategic concepts to diverse audiences
Experience or familiarity with agile methodologies and strong organizational skills