Analytics Engineering Manager, Data Platform & Governance

About This Opportunity

We are representing a fast-growing technology client in the home services sector. They operate a data-driven marketplace platform at significant scale, and their analytics function powers decision-making across product, marketing, operations, and finance.

Our client has built a solid data foundation—a centralized Redshift warehouse, dbt transformation layer, Airflow orchestration, and Segment event tracking—but they face a common challenge: everyone on the analytics team is an analyst. Data quality, governance, and platform health happen as side work, squeezed between analyses. They need someone who wakes up thinking about making data trustworthy.

This is a hands-on Analytics Engineering Manager role focused on data platform governance. You will be the first person dedicated to ensuring their data can be trusted—owning data quality, tracking standards, BI platform health, and the roadmap that makes the platform more reliable every quarter.

Responsibilities

Own the data roadmap. Discover what product, marketing, operations, and finance need from data. Prioritize those needs against platform health. Sequence the investment. Present it, defend it, and re-plan as the business evolves.

Ensure data quality and freshness. Build automated monitoring across source data, pipelines, and reports. Catch upstream schema changes before they break anything downstream. Run incidents to resolution when they happen.

Establish data lineage and impact analysis. Create a living map from production source to warehouse model to dashboard. Ensure that when a production change is proposed, its downstream impact on pipelines, metrics, and reports gets assessed before it ships. Work toward data contracts with engineering so breaking changes get caught in their workflow, not yours.

Administer the BI migration. Lead the transition to Lightdash as the single BI platform, replacing legacy tools. Design workspace structure, permissions, and certification standards. Enable stakeholders to self-serve while keeping the environment organized and trustworthy.

Extend and guard the semantic layer. The client has shipped governed definitions for their most critical metrics. You will extend coverage to the full metric catalog and prevent uncontrolled growth as the layer scales.

Govern event tracking. Own the Segment event catalog. Review new events against standards. Keep the catalog matched to what production actually sends. Evolve guardrails as tracking grows.

Prepare data for AI consumption. AI agents query the warehouse daily through internal AI tooling. You will govern what data AI tools can access and keep the warehouse AI-legible: documented, consistent, and safe for automated querying.

Manage data security and privacy. Maintain access controls, handle PII appropriately under US state privacy laws, and conduct periodic reviews of who and which AI tools can access what data.

Build the governance system. Create documentation, ownership models, and review loops that keep all of the above running without heroics.

Start solo, then build a team. You will begin as the sole governance owner, working hands-on. Once you have landed the foundational systems, you will hire and manage a Lead Analytics Engineer. The team will grow as scope demands.

What We're Looking For

Governance is your craft. You genuinely enjoy making data systems trustworthy and organized. You cannot leave a broken naming convention alone. If you see governance as a stepping stone to "real" analytics work, this role is not a good fit.

AI-native approach. You use AI tools like Claude Code, Copilot, or ChatGPT daily to build quality checks, write automation, triage anomalies, and document as you go. You also understand that AI agents consume data, and making the warehouse safe and legible for them is part of modern governance.

Hands-on manager. You have been accountable for other people's output—allocating their time, owning their priorities—but you never stopped building yourself. You write SQL, debug Airflow DAGs, and configure permissions personally. If seniority took you away from the keyboard, or if you have never been responsible for anyone's work but your own, this role is not a good fit.

Product-minded. You start from what the business is trying to decide, not from what the pipeline does. You can turn a vague stakeholder request into a prioritized plan. If you need requirements handed to you, this role is not a good fit.

Automation-first. Your instinct for any recurring check is to build a monitor, not a checklist. If your quality practice depends on manual review and discipline, this role is not a good fit.

An enforcer people like. You will hold engineers and analysts you do not manage to standards. This requires clear rules, good tooling that makes compliance easy, and the ability to say no gracefully. If you avoid friction or enjoy being the "department of no," this role is not a good fit.

Technical experience:

• Hands-on depth in data warehouse and pipeline layers (Redshift, dbt, Airflow or similar)
• Credible experience maintaining BI tools and tracking standards at company scale
• Experience with event tracking platforms like Segment
• Familiarity with BI tools such as Lightdash, Tableau, Metabase, or similar
• Experience building data quality monitoring and observability

This role is not: a big-team leadership position, a policy or committee job, a BI analyst role building dashboards for stakeholders, or a finished system to babysit. Much of this does not exist yet. If you want to operate a mature platform rather than build one, you will be frustrated.

How We Work With You

Candidates apply directly through Get A Job.ai. Our talent team will conduct an initial screening to understand your background and confirm fit for the role. If there is mutual interest, we will submit your profile to our client for their review. We coordinate all interview scheduling and feedback between you and the client.

Please do not contact the client directly. All communication regarding this opportunity should go through Get A Job.ai to ensure a smooth and professional process for everyone involved.

Pay

Pay via Get A Job.ai: $75,000–$120,000 per year.

This is a fully remote position. The work requires deep focus—building monitors, untangling pipelines—and the client trusts you to manage your environment. Async collaboration is the norm. Flexible PTO is provided; the focus is on results.

Equal Employment Opportunity: Get A Job.ai provides equal employment opportunities to all employees and applicants without regard to race, color, religion, sex, national origin, age, disability, or genetics. We comply with applicable state and local laws governing nondiscrimination in employment.