Manager, Software Engineering (Data Engineering)

• Key Responsibilities (Leadership‑ Focused)  Lead and grow the team - coach and develop data engineers through regular 1:1s, feedback, and career development, building an engaged, inclusive, high‑ performing team.  Own delivery outcomes - ensure teams consistently meet commitments on data quality, reliability, security, and time‑ to‑ market for data pipelines, platforms, and products.  Provide technical direction - guide architecture and design decisions across data pipelines, data models, and data platforms, ensuring solutions are robust, maintainable, and cloud‑ ready.  Set engineering standards - define and enforce standards for data engineering design, coding, code review, testing, documentation, and operational readiness.  Drive DevSecOps and data delivery practices - embed secure engineering, automated testing, data validation, and policy checks into CI/CD pipelines and data deployment workflows using Git‑ based tooling.  Own cloud‑ based data delivery - ensure data solutions are designed and operated for cloud, leveraging managed data services, lakehouse patterns, and infrastructure‑ as‑ code where appropriate.

• Leadership & Management  8+ years of experience delivering software or data solutions using Agile, Lean, and DevSecOps practices.  3+ years of direct people leadership experience managing engineering teams (data engineering preferred), including hiring, coaching, performance management, and career development.  Proven ability to build and sustain high‑ performing, inclusive teams and to lead through change (e.g., modernization, cloud adoption, data platform transformation).  Demonstrated strength in execution leadership-setting clear goals, making timely decisions, managing risk, and honoring commitments across multiple initiatives.  Experience influencing and partnering in matrixed environments with product, architecture, analytics, security, infrastructure, and business stakeholders; vendor or managed‑ service experience is a plus.  Financial services or insurance industry experience is preferred but not required.

Cloud‑ native data architectures and platforms o Practical experience designing and running data workloads on major cloud platforms (preferably AWS, with experience on other major cloud providers acceptable), using services such as object storage, data integration, compute, and orchestration. o Experience with Databricks (or similar Spark‑ based platform) for large‑ scale batch and streaming data processing, including notebooks, jobs, cluster configuration, and performance optimization. o Experience with modern cloud data warehouses, preferably Snowflake (or equivalent), including data modeling and performance tuning is a plus. o Ability to design data solutions for scalability, resiliency, and performance (e.g., partitioning, clustering, caching, schema evolution, and cost optimization practices).  Languages, tooling, and DevSecOps for data o Proficiency in at least one modern programming language used for data engineering, such as Python, Scala, or Java. o Strong experience with SQL for data transformation, analysis, and performance tuning. o Experience with Git‑ based workflows (branching strategies, pull requests, code review) and CI/CD pipelines for data engineering (build, test, deploy, and validation automation). o Comfort automating data quality and governance gates (e.g., unit/integration tests, data validation checks, schema enforcement, lineage tracking) as part of delivery pipelines.  Security, privacy, and compliance (mandatory) o Strong understanding of data secu