Senior Manager, Data Engineering

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Keyloop bridges the gap between dealers, manufacturers, technology suppliers and car buyers. We empower car dealers and manufacturers to fully embrace digital transformation. How? By creating innovative technology that makes selling cars better for our customers, and buying and owning cars better for theirs. We use cutting‑edge technology to link our clients’ systems, departments and sites. We provide an open technology platform that’s shaping the industry for the future.

We use data to help clients become more efficient, increase profitability and give more customers an amazing experience. Want to be part of it?

Join us to build a world‑class data and analytics platform that will reshape automotive retail. You’ll lead the engineering discipline that powers our data lake, warehouse, real‑time clickstream, and applied AI—turning large‑scale data into trusted, high‑impact products our customers use every day.

Data Platform Engineering Team – Own our AWS‑based datalake, warehouse and core data infrastructure, including security, reliability, cost efficiency and scalability.

Analytics Engineering Team – Drive data modelling, transformation and pipeline standards through the lake/warehouse to power governed, reusable semantic layers and metrics.

Clickstream Team – Operate and evolve our real‑time event ingestion and processing to bring product telemetry and digital signals into the platform.

• Run the function day‑to‑day. Plan, prioritise and deliver across the teams and ensure resilient operations.
• Set platform direction. Support Data Architects and contribute to the modern data architecture on AWS across storage, compute, streaming, governance and cataloguing; align designs to the AWS Well‑Architected Framework and the Data Analytics Lens.
• Raise engineering standards through Embed IaC, CI/CD for data, MLOps, testing, observability, lineage and cost/Fin Ops; codify patterns for batch, streaming and ML.
• Deliver trustworthy data. Support the teams to establish modelling conventions (e.g., layer boundaries, naming, contracts), data quality SLAs, and a governed metrics layer consumable by our Thought Spot visualisation platform.
• Security, privacy & compliance. Champion least‑privilege access, encryption, auditability and privacy‑by‑design across all datasets.
• People & performance. Lead managers and senior ICs; grow capability through coaching, clear career paths, hiring, and a high‑performance, inclusive culture.
• Stakeholder leadership. Partner with Product, Platform, External 3rd Parties and Customer teams to convert business goals into a data roadmap, measurable outcomes and transparent delivery.

• Proven leadership of multi‑team data engineering organisations (platform, analytics engineering, streaming and ML/AI) in a product‑led, cloud‑native environment.
• Data Architecture and operational expertise with AWS analytics services and modern data architecture (lake + warehouse + streaming under unified governance).
• Strong track record shipping reliable, cost‑efficient pipelines at scale; credibility in SQL/Python and with at least one of dbt/Spark.
• MLOps experience: feature pipelines, automated testing, model versioning/registry, promotion workflows and post‑production monitoring.
• Excellent people leadership: hiring, developing and performance‑managing senior engineers; setting clear goals and creating psychological safety.
• Effective stakeholder management and communication—from architecture decisions to executive updates.
• Nice to have: experience with Thought Spot, automotive or adjacent domains.

Heavily AWS‑based (e.g., S3, Lake Formation, Glue, Athena/Redshift, EMR, Kinesis); transformation and modelling (SQL, dbt, Spark); orchestration (Airflow/Dagster/Prefect); languages (Python/SQL); CI/CD & IaC (Git Hub/Git Lab, Cloud Formation); observability (Cloud Watch + data quality/lineage tooling);
Thought Spot as the visualisation layer; MLOps (Sage Maker and/or MLflow Model Registry).

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