Software Development Manager

Capital Group•Seattle, WA

11d•Hybrid

About The Position

We are building and evolving a modern, scalable data and analytics ecosystem to support mission‑critical investment and investor‑facing applications. As a Software Development Manager within IG Data, you will lead a team of software and data engineers responsible for an analytics platform and API layer that delivers reliable, high‑performance, and time‑sensitive insights to Capital Group’s business and technology partners. In this role, you will partner closely with business leaders, product managers, and engineering peers to shape product vision and translate business objectives into scalable, secure, and well‑engineered data solutions. You will apply strong technical judgment and a systems‑engineering mindset to ensure platforms are performant, resilient, and built for long‑term sustainability. You will play a key role in the design and implementation of Capital Group’s next‑generation data platform, leveraging modern cloud‑native technologies and tools such as AWS, Databricks, Kubernetes, and workflow orchestration frameworks. You are a leader who ensures teams deliver with speed and quality while continuously improving engineering practices, data reliability, and developer productivity.

Requirements

Demonstrated success leading software and data engineering teams delivering scalable, production‑grade platforms.
Strong experience designing and operating cloud‑native data platforms on AWS, including Kubernetes‑based workloads and API services.
Deep technical knowledge of modern data stacks, including Databricks, dbt, Airflow, relational databases, and SQL performance optimization.
Apply a systems‑engineering mindset, understanding end‑to‑end data flows and driving improvements across ingestion, processing, and serving layers.
Strong track record of delivering commitments, balancing speed, quality, and long‑term maintainability.
~7+ years of experience with ~3+ years in leading and mentoring engineers, supporting career growth, and building high‑performing, engaged teams.
Bachelor's degree in information technology, computer science, or a related field.
Sound judgment, strong communication skills, and the ability to influence across product, engineering, and business stakeholders.
Value continuous learning and regularly evaluate new tools and technologies to improve engineering effectiveness and data capabilities.

Responsibilities

Lead, mentor, and develop a team of software and data engineers, fostering a culture of engineering excellence, collaboration, and continuous improvement.
Partner with product, analytics, and platform teams to define data strategy and translate business requirements into scalable technical solutions.
Drive hiring, onboarding, performance management, and career development for a growing engineering organization.
Design and oversee cloud‑native data architectures on AWS, leveraging services such as EKS, S3, RDS/Aurora PostgreSQL, and related services.
Establish best practices for infrastructure‑as‑code using tools such as Terraform to ensure repeatable, auditable deployments.
Manage cost optimization, security, and performance across cloud data workloads in partnership with platform engineering teams.
Lead the design and operation of containerized data workloads on Amazon EKS, ensuring scalability, resilience, and efficient resource utilization.
Define standards for deploying and operating data services on Kubernetes, including autoscaling, resource governance, and CI/CD integration.
Architect and guide the development of data‑centric APIs that provide reliable, low‑latency access to downstream consumers.
Champion observability practices, including logging, monitoring, and alerting across data services.
Own and evolve the Databricks platform, including cluster management, governance, and optimization of Spark workloads.
Drive adoption of dbt as the standard transformation layer, establishing modeling, testing, and documentation standards.
Oversee workflow orchestration using Apache Airflow, ensuring pipelines are reliable, maintainable, and aligned with SLA expectations.
Establish data quality frameworks including validation, anomaly detection, and monitoring across critical data products.
Drive execution through effective software development methodologies, continuous process improvement, and thoughtful system refactoring.
Own velocity, quality, and reliability metrics, recognizing they are complementary and mutually reinforcing.
Resolve root causes of systemic issues across technology, process, and team dynamics.
Contribute to product roadmaps by balancing near‑term business impact with long‑term platform health and scalability.