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About Measured
Measured is the pioneer and leader of incrementality-based media measurement and optimization. Since 2017, leading brands have used our AI-powered, all-in-one platform to manage, test, plan, and optimize over $35 billion in full-funnel media investments. Measured 's unique combination of automated experimentation, media mix modeling, and industry-leading expertise helps marketers prove the incremental impact of their advertising and maximize ROI with unmatched ease, accuracy, and efficiency.
The Role
This position reports to a Director, Technical Implementations, on the team that takes every new Measured client from signed contract to a fully connected, fully loaded, and QA'd data foundation — owning the entire onboarding journey from source connections through historical ingestion, normalization, and output QA. As a Senior Technical Implementations Specialist, you will own discrete workstreams within our largest enterprise engagements and own lower-complexity client onboardings end to end, sitting directly with client data engineering and marketing program teams to explain precisely what Measured needs and why, then building it yourself. Just as importantly, you will act as a consultant to clients on preparing their data to meet Measured 's model requirements — representing in detail how our product and models actually consume data, and using that expertise to judge where a requirement is firm because the model breaks without it, and where a practical tradeoff costs little. Knowing that difference, explaining it, and standing behind the call is central to this role.
This role suits a seasoned, genuinely client-facing implementation professional who is hands-on with data — someone who leads client working sessions with confidence today and is on a path toward owning full enterprise onboardings start to finish tomorrow, with a Director remaining accountable for the overall program as you build toward that.
At Measured , we equip every team member with best-in-class technology to fuel extraordinary outcomes. We expect our team to continuously evolve alongside these tools, using AI as a partner for momentum and innovation — raising the bar on everything we build. Our people own the final voice, judgment, and accountability behind every deliverable.
Requirements
Key Responsibilities
Onboarding Delivery & Workstream Ownership
Own named workstreams within enterprise onboardings (source connections, historical ingestion, normalization specs, QA) under a Director, or own end-to-end onboarding for lower-complexity clients from kickoff through dashboard-ready output
Build and maintain a shared delivery plan for your workstreams — dependencies, owners, dates, blockers — and drive them to committed go-live dates while running several concurrent onboardings without dropping threads
Surface scope, data-quality, and timeline risk early, with a recommendation attached rather than just a flag
Data Requirements Consultancy & Model Readiness
Act as the client's consultant on preparing data to meet Measured 's model requirements — advising on structure, granularity, historical depth, and field-level detail so gaps are designed out rather than discovered at QA
Distinguish firm requirements from acceptable tradeoffs, make and defend those calls in the moment with a clear account of what's gained and given up, and push back on requests that would compromise model integrity by offering a workable alternative
Judge whether a client data limitation is a genuine blocker or manageable caveat and set expectations before go-live; escalate precedent-setting tradeoffs to your Director with a recommendation
Data Source Connection & Historical Ingestion
Scope each client's required feeds (media/vendor cost, transaction/source-of-truth, organic, custom), determine the right ingestion method, and stand up connections across APIs, file-based feeds, warehouse shares, and direct integrations
Land the full history each feed requires — not just go-forward — and prove completeness against the client's own reporting
Diagnose ingestion failures (auth, schema drift, partial files, delivery gaps, silent upstream changes), partner with client-side engineers and vendors to resolve them, and establish monitoring so feeds stay healthy post-go-live
Data Normalization & Specification
Author and maintain the specification layers mapping raw client data into Measured 's normalized model, translating inconsistent naming and taxonomy into structured, rules-driven mappings
Assess every spec change for downstream impact before it goes live, and balance client-specific accuracy against patterns reusable across the book of business
Document mapping logic and the reasoning behind judgment calls so the next person can maintain it
QA & Output Validation
Own QA for your workstreams — the data isn't done until it ties out against client source-of-truth, vendor exports, and upstream feed tables
Write advanced SQL to validate transformations, surface anomalies, quantify variance, and isolate root cause
Define and document acceptance criteria, and sign off with a written statement of coverage, known gaps, and caveats
Client Partnership & Communication
Serve as a credible, trusted technical presence for client stakeholders across marketing, analytics, and data engineering, representing Measured with polish in front of enterprise stakeholders, including in difficult conversations
Independently lead client-facing working sessions (discovery, spec reviews, QA walkthroughs, go-live readiness), translating technical requirements into terms marketers can act on and vice versa
Manage client expectations on timelines and dependencies with transparency, and gather/synthesize client feedback to inform product and onboarding improvements
Subject Matter Expertise & Technical Depth
Develop deep expertise in Measured 's data pipelines, normalization layers, and platform architecture, and become the go-to expert on specific feed types, vendor integrations, or client verticals
Understand incrementality testing and media mix modeling well enough to know why the data has to be shaped the way it is, maintaining a working command of what each Measured product requires from the data — and the reasoning behind it — so client guidance is grounded in the model rather than convention
Stay current on product roadmap and platform changes that affect how client data must be prepared, partnering with Product and Engineering to translate technical architecture into client-facing guidance
Serve as the internal reference point when data-requirements questions arrive from Sales, Solutions, and Customer Success
Contribute to technical documentation, implementation runbooks, and internal training materials, and represent the implementation perspective in cross-functional initiatives and product development discussions
Create and maintain implementation runbooks, spec-writing standards, onboarding playbooks, and process documentation
Document recurring data issues with root cause analysis and prevention strategies
Share hard-won client and feed knowledge so no onboarding depends on one person's memory
Serve as a second pair of eyes for peers on complex specs and QA logic
Ensure documentation stays current with product and pipeline changes
Model excellence in client communication, technical depth, and operational discipline
Process Improvement & Repeatability
Streamline onboarding by automating manual, repeated, or error-prone steps, and build reusable templates, spec patterns, and validation queries that shorten time-to-value for future clients.
Improve quality and scalability by catching requirements gaps at discovery rather than QA, surfacing cross-client issues as product/pipeline asks, and partnering with your Director and VP, Technical Implementations, on strategic initiatives like data health monitoring, proactive alerting, QA frameworks, and applying agentic AI approaches across operations.
Cross-Functional Collaboration
Partner with Customer Success on onboarding status, launch readiness, and client health; support Sales and Solutions during technical scoping and pre-sales data-feasibility discussions
Work with Engineering and Data Science on pipeline changes, ingestion defects, and model-readiness of client data, and with Product to communicate client pain points, feature requests, and usability feedback
File and shepherd well-specified internal requests — with sample rows and expected behavior — so downstream teams can act without a follow-up round, and contribute to cross-functional projects that improve onboarding experience and platform reliability
Ideal Experience
Required Experience
5+ years of experience in technical implementation, data onboarding, solutions consulting, or technical account management in B2B SaaS, ideally with enterprise clients 3+ years in a directly client-facing role owning technical delivery with external stakeholders
Extensive client-facing experience - comfortable leading working sessions, spec reviews, and candid timeline conversations with enterprise stakeholders, and equally comfortable doing the hands-on data work yourself
Consultative judgment with data requirements - demonstrated experience advising clients on how to prepare data for a specific analytical or modeling purpose, including telling a client no and putting a workable alternative in front of them
Strong technical proficiency in data platforms, SQL, APIs, and integrations with hands-on build and troubleshooting experience Experience with marketing technology platforms including advertising platforms (Facebook, Google, TikTok) and analytics tools (GA4, Adobe Analytics) Proven ability to diagnose and resolve complex data issues including data pipeline problems, integration errors, schema mismatches, and incomplete historical loads Experience owning delivery of complex, multi-stakeholder projects against committed timelines Experience guiding peers or client teams through technical processes they don't own Working proficiency with project and issue tracking (Jira, Asana, or similar) to run multi-workstream delivery Track record of driving process improvements that measurably improve team efficiency or delivery speed Excellent written and verbal communication skills with ability to explain technical concepts to diverse audiences Client-first mindset with proven ability to build trusted relationships with technical and non-technical stakeholders alike BA/BS preferred; background in technical field, data analytics, or related discipline
Technical Skills
Advanced SQL proficiency for querying, validating transformations, reconciling loaded output against source, and diagnosing data quality issues, plus the ability to read technical logs, error messages, and pipeline diagnostics
Strong understanding of ETL/ELT pipelines, data warehouses, and integration patterns, including cloud data platforms (Snowflake, BigQuery, Databricks, Redshift) and familiarity with orchestration tooling (Airflow or similar) a plus
Experience with APIs and integrations (authentication, rate limits, error handling) and file-based data delivery (S3, SFTP, cloud storage), including file spec design, schema definition, and delivery troubleshooting
Practical skill in data mapping and normalization — building rules-driven logic that turns inconsistent client taxonomies into a clean model — and experience reconciling transaction/source-of-truth data to a client's own reported numbers
Knowledge of marketing analytics concepts including attribution, incrementality, and media mix modeling; familiarity with data visualization tools (Tableau, Looker, Mode) and scripting/analysis tools (Python, R) a plus; understanding of data privacy regulations (GDPR, CCPA); and comfort running several onboardings or workstreams concurrently, each at a different phase
Ownership & Influence Skills
Proven ability to drive delivery through people you don't manage - client teams, vendors, and internal partners
Strong facilitation instincts - keeps a working session on track and ends it with decisions and owners
Knowledge-sharing mindset that elevates team capabilities through documentation and training
Comfortable pushing back - on a client's assumption, an unrealistic date, or a spec that won't hold up - and can name the model-level reason why
Leads by example, modeling excellent client communication, technical rigor, and operational discipline
Collaborative working style that inspires confidence and builds trust on both sides of the engagement
Key Attributes
Technical Expert: Deep curiosity about how systems work with commitment to continuous technical learning
Client Champion: Passionate about delivering exceptional experiences and building client trust
Strategic Thinker: Sees beyond the single feed to identify patterns, systemic issues, and reusable solutions
Sound Judgment: Knows the difference between a requirement that cannot bend and one that can, and can explain which is which to a client without hedging
Clear Communicator: Translates technical complexity into clarity for clients and team members alike
Ownership Mindset: Takes full accountability for delivery, quality, and the commitments made to the client. Drives work to a verified, signed-off end state rather than a mostly-working one
Calm Under Pressure: Maintains composure and clarity during compressed timelines, shifting client requirements, and high-visibility launches Growth-Oriented: Proactively identifies and drives improvements to processes, documentation, and team capabilities. Receptive to feedback and committed to continuous improvement Leads with integrity and embraces diversity in all forms
Benefits
100% Remote
Competitive Total Rewards and flexible paid time off
Opportunities to give back through Measured for Good
Engaged, diverse, and curious culture
Award-winning technology powered by an agile, collaborative team