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.

Technical Skills - Data Engineering :

Modern data engineering and platforms :

1. Strong, hands on background earlier in career in data engineering, including building and operating data pipelines, data products, and/or data platforms.

2. Proven experience designing and reviewing data solution designs, data models, and pipeline implementations, challenging trade offs, and providing credible technical guidance to engineers.

Cloud native data architectures and platforms :

1. 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.

2. Experience with Databricks (or similar Spark?based platform) for large's cale batch and streaming data processing, including notebooks, jobs, cluster configuration, and performance optimization.

3. Experience with modern cloud data warehouses, preferably Snowflake (or equivalent), including data modeling and performance tuning is a plus.

4. 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 :

1. Proficiency in at least one modern programming language used for data engineering, such as Python, Scala, or Java.

2. Strong experience with SQL for data transformation, analysis, and performance tuning.

3. Experience with Git?based workflows (branching strategies, pull requests, code review) and CI/CD pipelines for data engineering (build, test, deploy, and validation automation).

4. 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) :

1. Strong understanding of data security and privacy practices-encryption, key management, least?privilege access, secrets management, data masking, and handling of PII/PCI.

2. Experience working in regulated environments, aligning with internal security standards, risk controls, and audit requirements (e.g., logging, traceability, retention, change management).

Integration, data consumption, and observability :

1. Experience integrating data platforms with upstream and downstream systems via batch interfaces, APIs, messaging/event streams, or data sharing mechanisms.

2. Working knowledge of relational databases and at least one NoSQL or cloud?native data store, with an understanding of schema design and performance considerations.

3. Familiarity with observability tooling and practices for data systems (logs, metrics, traces, data?quality dashboards, alerts) to support reliable operations and fast incident response.

Nice to have (plus skills) :

1. Experience with additional data tools such as dbt, Great Expectations, Soda, Airflow, or similar orchestration and data quality frameworks is a plus.

2. Exposure to machine learning, analytics, or BI use cases and enabling self's ervice data consumption for analysts and data scientists is a plus.