Senior Data Engineer at Kargo

London, England, United Kingdom -

Full Time

Start Date

Immediate

Expiry Date

19 Sep, 26

Salary

0.0

Posted On

21 Jun, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, Spark, Airflow, Apache Iceberg, AWS, EKS, Docker, Kubernetes, ArgoCD, Prometheus, Snowflake, ClickHouse, SQL, Kafka, Flink, Aerospike

Industry

Logistics;Transportation;Supply Chain and Storage

Description

Who We Are Kargo creates powerful moments of connection between brands and consumers to build businesses. Every day, our 600+ employees work to radically raise the bar on what agentic AI, CTV, eCommerce, social, and mobile can do to deliver unique ad experiences across the world’s most premium platforms. Taking a creative science approach to all we do, we continuously innovate solutions that outperform industry benchmarks and client expectations. Now 20+ years strong, Kargo has offices in NYC, Chicago, LA, Dallas, Sydney, Auckland, London and Waterford, Ireland. Who We Hire Techies who want to build the future. Creatives who want to design it better. Communicators to win business. Collaborators to build it. Data pros who turn numbers into insights. Product builders who turn ideas into innovations. Anyone eager to be on a team that doesn’t stop to ask what’s next, because they’re already building it. Mission The Senior Data Engineer exists to build and scale the data infrastructure that powers Kargo's targeting, identity, and measurement capabilities. This role owns hands-on implementation of the pipelines, systems, and tooling that turn raw data into competitive advantage — at speed and at scale. When this role is firing, data flows reliably, engineering teams move faster, and Kargo's data products are built on a foundation that can handle whatever comes next. This is a remote role in London, UK. Outcomes — What Success Looks Like in 6–12 Months Core ETL/ELT pipelines are optimized and operationally reliable — Existing pipelines are assessed, bottlenecks are addressed, and data flows with documented SLAs; incidents are caught proactively, not reactively New datasets are onboarded and powering targeting capabilities — At least one meaningful new data source is integrated end-to-end, with targeting or measurement use cases activated and performing against defined KPIs CI/CD practices are adopted and accelerating team velocity — ArgoCD-based deployment pipelines are in place and the team is shipping with greater speed and fewer rollback events as a result Privacy-compliant data methodologies are implemented and documented — Modern approaches to identity and targeting that meet GDPR/CCPA standards are built into the stack, not bolted on after the fact Technical documentation is thorough and actually used — Implementation docs, runbooks, and best practices are written clearly enough that a new engineer can onboard to a system without a walkthrough Skills — Core Technical Capabilities Required Deep proficiency in Python and Spark for building and optimizing large-scale data pipelines; strong grasp of data structuring for performance and efficiency Hands-on experience with Airflow for orchestrating complex data workflows, and Apache Iceberg for table format management at scale Extensive AWS experience including EKS, Docker, and Kubernetes; comfortable owning infrastructure deployment and scaling decisions CI/CD fluency — ArgoCD, automated testing pipelines, and monitoring via Prometheus; treats deployment reliability as a first-class concern Familiarity with analytical warehousing in Snowflake or ClickHouse — SQL optimization, cost management, and performance tuning Working knowledge of identity, privacy, and targeting methodologies in ad tech — understands how data flows through the ecosystem and where privacy constraints apply Preferred Experience with real-time streaming technologies — Kafka, Flink — for latency-sensitive data processing use cases Familiarity with Aerospike or similar low-latency key-value stores used in high-throughput targeting systems Competencies — Behaviors We Like to See Hands-On Ownership Drives implementation independently from ambiguous brief to working system — doesn't wait for perfect specs to start building Takes operational responsibility for the systems they build; monitors, iterates, and improves without being prompted Rigorous Problem-Solving Brings structured thinking to complex, multi-system challenges; breaks down problems clearly before reaching for a solution Optimizes for the right outcome — performance, cost, reliability — not just the fastest path to "done" Technical Collaboration Contributes actively in design and brainstorming sessions; brings strong opinions and is equally willing to update them when better ideas emerge Communicates technical decisions clearly to both engineering peers and non-technical stakeholders without oversimplifying Documentation and Knowledge Sharing Writes documentation that actually gets used — clear, thorough, and maintained as systems evolve Treats onboarding others as part of the job; leaves systems better understood than they found them Our Laurels AdAge Best Places to Work ThinkLA Partner of the Year Built In Best Places to Work Cynopsis 2025 Top Women in Media - Jeannine Shao Collins Martech Breakthrough Awards - Best Overall Adtech Company Digiday Media Awards Best Event Cynopsis Media Impact Awards-Best CTV Platform Martech Breakthrough Awards-CTV Innovation Adweek Media Plan of the Year Awards - Best Use of Insights Follow Our Lead Big Picture: kargo.com The Latest: Instagram (@kargomobile) and LinkedIn (Kargo) Kargo is an Equal Opportunity Employer. We are committed to building an inclusive and diverse workplace where all employees and applicants are treated with respect and dignity. We do not discriminate on the basis of race, color, ethnic origin, religion or belief, sex, sexual orientation, gender identity or expression, age, disability, marital or family status, national origin, veteran status, or any other characteristic protected by applicable local, state, or federal law. All qualified applicants will receive consideration for employment. Pursuant to applicable fair chance laws, including the San Francisco Fair Chance Ordinance and the Los Angeles Fair Chance Initiative for Hiring, Kargo will consider qualified applicants with arrest and conviction records for employment.

Responsibilities

Build and scale data infrastructure to power targeting, identity, and measurement capabilities. Own the implementation of ETL/ELT pipelines and CI/CD practices to ensure reliable data flow and team velocity.