Data Engineering Manager

📋 Description

• Lead and nurture a team of data engineers, fostering execution, operational excellence, and sustainable growth within the data engineering field.

• Take responsibility for the reliability, scalability, and advancement of Kin’s data platform infrastructure and lakehouse environment utilizing Databricks.

• Design and enhance batch, streaming, and event-driven data pipelines employing technologies such as PySpark, Databricks, SQLMesh, Kafka, and Fivetran.

• Establish engineering standards for testing, observability, CI/CD, monitoring, incident response, and operational reliability across the data platform.

• Define and implement governance, lineage, classification, retention, and access control protocols in collaboration with Security, Legal, and Compliance teams.

• Promote infrastructure-as-code and GitOps methodologies for platform resources using tools such as Terraform.

• Collaborate with application engineering teams to create robust integrations between source systems and the data platform while proactively managing schema and dependency modifications.

• Work alongside analytics engineering, product, and data science stakeholders to align platform investments with business priorities and downstream data requirements.

• Lead the team through large-scale technical projects with clear prioritization, iterative delivery, and informed operational trade-off decisions.

• Identify opportunities to enhance engineering efficiency and platform scalability through automation, tooling enhancements, and strategic use of AI-enabled workflows when suitable.

⛳️ Requirements

• 6–8+ years of experience in data engineering, platform engineering, or infrastructure-focused data roles, including experience in managing and developing data engineering teams.

• Practical experience in building and operating modern data platforms utilizing technologies such as Databricks, Snowflake, BigQuery, or similar lakehouse and warehouse ecosystems.

• Experience in designing and maintaining scalable batch, streaming, and event-driven data pipelines using tools such as PySpark, SQLMesh, Kafka, Fivetran, or Databricks Jobs.

• Familiarity with implementing CI/CD, automated testing, monitoring, and observability practices for data infrastructure and pipelines.

• Experienced in defining and executing data governance, privacy, classification, retention, and access control frameworks in regulated or compliance-sensitive settings.

• Proficient in Python and PySpark for large-scale data engineering applications.

• Experience managing infrastructure-as-code and GitOps workflows using tools such as Terraform.

• Ability to collaborate effectively across both technical and non-technical teams while navigating ambiguity, operational trade-offs, and competing priorities.

• Proven experience in leading teams through complex platform migrations, architectural evolutions, or operational scaling initiatives.

🏝️ Benefits

• Competitive salary and company equity through Restricted Stock Units (RSUs), granted as part of our standard compensation package based on role and level.

• 401(k) with company match up to 4% of eligible earnings.

• Multiple medical plan options, as well as dental and vision coverage.

• Company-funded HSA contributions based on medical plan selection.

• Company-paid life insurance and short-term disability coverage.

• A variety of supplemental benefit options, including long-term disability, critical illness, accident, legal, and pet insurance.

• Access to mental health support and confidential counseling resources.

• Flexible PTO for exempt employees (most employees take 15–20 days per year), in addition to 8 company-observed holidays.

• Paid parental leave, offering up to 14 weeks at 100% pay for birthing parents and 8 weeks at 100% pay for non-birthing parents.

• Opportunities for career mobility and internal growth within the organization.

• Professional development budgets for certifications, conferences, and learning opportunities available, subject to management approval.