Manager - Software Engineering (Data Engineering)

Our client is seeking a Manager, Software Engineering (Data Engineering) to lead high ‑ performing data engineering teams delivering secure, scalable, and reliable data products, pipelines, and platforms that enable critical analytics, AI/ML, and digital experiences. This role is responsible for building and operating production‑grade data solutions that power key customer and associate outcomes.

As a technical manager, you will spend most of your time leading and developing data engineers, while also providing hands‑on technical direction-shaping data architectures, guiding design and code quality, and ensuring robust data engineering practices on cloud. You are accountable for delivery outcomes (data quality, reliability, security, and time‑to‑market) and for raising the engineering bar across the data engineering stack.

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.
Strengthen production operations - champion observability, performance, resiliency, incident management, and post‑incident learning for data platforms and pipelines in production.
Use metrics to improve - track and act on delivery, data quality, stability, and productivity metrics to refine ways of working and remove bottlenecks.
Manage capacity and sourcing - plan and manage staffing, capacity, and workforce mix (associates and vendors/supplemental resources) to align with roadmap and demand.
Partner across the ecosystem - work closely with product, architecture, analytics, security, infrastructure, and business stakeholders to align priorities, manage dependencies, and communicate risks and trade‑offs clearly.
Foster a culture of learning and experimentation - encourage engineers to grow skills in cloud, data platforms, and automation, and to share knowledge across teams.
May perform other responsibilities as assigned.

Reporting Relationships

Reports to a Technology Director or Associate Vice President within the company.

Leads a team of data engineers and related technology professionals and may provide direction to vendor or managed‑service teams.

Qualifications & Experience:

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

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

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.
Experience with Databricks (or similar Spark‑based platform) for large‑scale batch and streaming data processing, including notebooks, jobs, cluster configuration, and performance optimization.
Experience with modern cloud data warehouses, preferably Snowflake (or equivalent), including data modeling and performance tuning is a plus.
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

Proficiency in at least one modern programming language used for data engineering, such as Python, Scala, or Java.
Strong experience with SQL for data transformation, analysis, and performance tuning.
Experience with Git‑based workflows (branching strategies, pull requests, code review) and CI/CD pipelines for data engineering (build, test, deploy, and validation automation).
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)

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

Experience integrating data platforms with upstream and downstream systems via batch interfaces, APIs, messaging/event streams, or data sharing mechanisms.
Working knowledge of relational databases and at least one NoSQL or cloud‑native data store, with an understanding of schema design and performance considerations.
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)

Experience with additional data tools such as dbt, Great Expectations, Soda, Airflow, or similar orchestration and data quality frameworks is a plus.
Exposure to machine learning, analytics, or BI use cases and enabling self‑service data consumption for analysts and data scientists is a plus.

Mindset & Ways of Working

Demonstrated commitment to engineering excellence, automation, and continuous improvement.
Builds inclusive, high‑trust teams, supporting diversity of thought and background.
Comfortable balancing strategic thinking with rolling up sleeves to remove technical and delivery blockers.
Operates with a strong sense of ownership, accountability, and service to customers and business partners.

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Manager Data Engineering