Our client is looking for a Lead Software Engineer who is equal parts creative architect and hands‑on builder. This is not a role for someone who delegates from a distance—it is for a craftsperson who thrives when writing code, wrestling with complex data problems, and setting the technical standard for a team of five engineers.
You will own the design and development of scalable, data‑intensive applications that power their identity and analytics platform. You will drive the integration of AI and machine learning capabilities into production systems, modernize legacy infrastructure, and lead by example through code reviews, architecture decisions, and a relentless curiosity about what’s possible with big data.
If you are energized by the challenge of transforming massive, messy datasets into elegant, high‑performance applications—and you want to do it at a company where your architectural instincts will directly shape the product—this role is built for you.
Serve as the primary technical lead for a team of five engineers—setting standards, conducting code reviews, and mentoring through hands‑on collaboration, not just oversight.
Design scalable system architectures that balance performance, maintainability, and the pragmatic realities of big‑data production environments.
Champion AI‑assisted development practices across the team: from LLM‑aided code generation and automated testing to intelligent refactoring and documentation workflows.
Own solution design end‑to‑end—from whiteboard to deployment—translating business requirements into elegant technical implementations.
Design and build high‑performance backend applications and APIs that leverage AWS Redshift, SQL Server, and Python‑based data pipelines at scale.
Develop and optimize near‑real‑time data pipelines and services capable of processing large‑scale identity and behavioral datasets with low latency and high reliability.
Build and maintain RESTful APIs and microservices that serve as the backbone of the targeting and measurement platform.
Integrate Python‑based AI/ML models into production systems, collaborating closely with data science teams to operationalize machine‑learning decisioning at scale.
Apply AI tooling—including intelligent query optimization, automated anomaly detection, and model‑assisted pipeline monitoring—to continuously improve system performance.
Lead the migration and enhancement of legacy systems toward cloud‑native, event‑driven architectures on AWS.
Evaluate and adopt emerging AI development tools—including code‑generation assistants, automated testing frameworks, and LLM‑integrated developer workflows—to raise team velocity and code quality.
Partner with product and data science stakeholders to translate analytical requirements into robust, scalable engineering solutions.
5+ years of backend software engineering experience, with a strong portfolio of data‑intensive application development.
Expert proficiency in Python and strong command of SQL; familiarity with Java or similar languages is a plus.
Hands‑on experience with AWS Redshift or equivalent cloud‑native analytical databases (Snowflake, BigQuery, etc.).
Proven ability to design scalable backend systems—APIs, microservices, data pipelines—in production environments handling large datasets.
Demonstrated experience leading or mentoring a development team, with strong code review and technical communication skills.
A genuine creative instinct for software architecture: you see big‑data complexity as an interesting puzzle, not a burden.
Bachelor’s or Master’s degree in Computer Science, Software Engineering, or a related technical field.
Must be authorized to work in the United States; no visa sponsorship available.
Active experience integrating AI tools into development workflows—LLM‑assisted coding, AI‑powered testing, or automated code review tooling.
Background in AI, machine learning, or MLOps, including operationalizing ML models in high‑throughput production systems.
Familiarity with identity resolution, audience targeting, digital attribution, or marketing data platforms.
Experience with event‑driven architectures, streaming data (Kafka, Kinesis), or real‑time processing frameworks.
Track record of modernizing legacy monolithic systems into cloud‑native microservices architectures.
Lead a team of five and leave a lasting architectural fingerprint on a platform that processes hundreds of millions of identity records.
Work at a company that actively invests in and encourages the use of AI to improve how engineers build and how systems perform.
Tackle genuinely hard data problems—big scale, regulated industries, real‑time requirements—with the autonomy to solve them creatively.
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