At a Glance
- Tasks: Build a cutting-edge identity graph and lead a team of data engineers.
- Company: Join Kargo, a leading AdTech company with a creative science approach.
- Benefits: Enjoy competitive salary, inclusive culture, and opportunities for growth.
- Other info: Be part of a diverse team committed to innovation and excellence.
- Why this job: Make a real impact in the AdTech space while innovating with data.
- Qualifications: Experience in large-scale data systems and proficiency in Python, Airflow, and Spark.
The predicted salary is between 75600 - 92400 £ per year.
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, e Commerce, 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 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, e Commerce, 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.
The Opportunity
Identity is central to how Ad Tech works today: advertisers want cross-surface reach, user-level measurement, and lower-funnel attribution.
Their data reaches us two ways, and each has a clear next step: direct onboarding currently runs on a partner’s identity spine and we want to build a new, multi-source graph of our own; DMP feeds are proven and now need to scale as we enter in-app inventory.
- The Daily To-Do
- Build a new identity graph.
Take stock of what we have today, set its direction, and sequence the rollout: identifier sync, translation, clustering (with Data Science), opt-out handling.
- Standardize partner and client onboarding across web, CTV and mobile identifiers, including cleanroom onboarding, so each new feed costs less to stand up than the last.
- Ready the identity audience data layer for self-serve: creation, activation, state, and the reporting clients will discover audiences through.
- Own and raise the bar on the domain's observability and alert response.
Inventory today's signals, monitors and alerts, centralize them, and bring each to standard: a freshness and quality commitment, context for AI-assisted triage, and a runbook.
- Lead and grow the domain's data engineers.
Define the standards for testability, cost efficiency and the patterns worth repeating, then raise the team to them through your own code, reviews, and knowledge-sharing, recording decisions in ADRs.
Qualifications
- You’ve designed and owned large-scale, interdependent data systems, including at least one you built from scratch, and you turn ambiguity into a sequenced roadmap with Product and Data Partnerships.
- You’ve led engineers, setting direction, reviewing work, developing people, while staying hands-on.
- You have mastery of Python, Airflow and Spark, and write transformations that are idiomatic, testable and tuned for cost and performance; you write SQL for Snowflake with the same discipline.
- You’re at home in AWS and Kubernetes, can read infrastructure logs to diagnose failures and slowness, and have worked with third-party APIs inside ingestion pipelines.
- You’re fluent with AI tooling in your own work, and you think about what makes a codebase legible to it.
- Strongly Preferred
- Identity resolution or graph work in Ad
Tech: matching, device and household graphs.
- Privacy and consent obligations: opt-outs, deletion, GDPR and CCPA.
- Data cleanrooms for partner or client onboarding.
- CI/CD with Git Hub Actions/Argo CD; monitoring with Victoria Metrics/Prometheus/Grafana.
- Nice To Have
- Iceberg or a comparable table format at production scale.
- Streaming or near-real-time processing (Kafka, Redpanda or similar).
- Low-latency stores such as Aerospike
- Experience with OLAP databases like Clickhouse.
- Our Laurels
- Ad Age Best Places to Work
- Think LA 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 (@kargo. hq) and Linked In (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.
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Lead Data Engineer - Identity employer: Kargo
Kargo is an exceptional employer that fosters a dynamic and innovative work culture, perfect for Senior Data Engineers looking to make a significant impact. With the flexibility of a remote role based in London, employees enjoy a collaborative environment that prioritises professional growth and development, alongside competitive benefits that support work-life balance. Join us to be part of a forward-thinking team that values your contributions and encourages you to push the boundaries of data engineering.
StudySmarter Expert Advice🤫
We think this is how you could land Lead Data Engineer - Identity
✨Get Involved in Data Science Meetups
Tap into local data science meetups or workshops to connect with fellow enthusiasts and professionals. These events are goldmines for networking, and sometimes even lead directly to job openings at companies like Kargo!
✨Show Off Your Projects
Start building a public portfolio showcasing your data science projects on platforms like GitHub or personal websites. Highlight unique analyses or models you've developed. This not only demonstrates your skills but also gets your name out there for roles like Lead Data Engineer - Identity at Kargo.
✨Leverage Professional Networks
Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like Kargo.
✨Apply Directly through Our Website
When you find a suitable opening like Lead Data Engineer - Identity at Kargo, make sure to apply directly through our website. It gives you an edge and shows you're keen to join our team. Plus, who doesn’t love a direct application? It’s easier than navigating through job boards!
We think you need these skills to ace Lead Data Engineer - Identity
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at Kargo, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Kargo. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Kargo
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Kargo!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
