Sr. Technical Implementations Specialist (US)

Measured•Austin, TX

1d•Remote

About The Position

Measured is a leader in incrementality-based media measurement and optimization, providing an AI-powered platform for brands to manage, test, plan, and optimize media investments. The platform helps marketers prove the incremental impact of advertising and maximize ROI. This role reports to a Director, Technical Implementations, and is responsible for the entire onboarding journey of new clients, from initial connections through data ingestion, normalization, and quality assurance. The specialist will own discrete workstreams within large enterprise engagements and handle smaller client onboardings end-to-end. Key aspects of the role include translating requirements, acting as a consultant on data preparation, and ensuring data integrity for Measured's models. The ideal candidate is a client-facing implementation professional with a strong background in marketing and media data, capable of leading client sessions and progressing towards owning full enterprise onboardings. Measured emphasizes continuous evolution with technology, including AI, and expects team members to own the final judgment and accountability for deliverables.

Requirements

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
Extensive experience with marketing technology platforms including advertising platforms (e.g. Facebook, Google, TikTok) and analytics tools (e.g. GA4, Adobe Analytics)
Working with data platforms, SQL, and integrations — enough to validate outputs and troubleshoot with engineering partners
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
Solid grounding in marketing analytics concepts including attribution, incrementality, and media mix modeling, plus hands-on experience with paid media platforms, campaign reporting, and budget/spend reconciliation; familiarity with data visualization tools (Tableau, Looker, Mode) a plus
Familiarity with using SQL to spot-check data and validate reporting outputs
General familiarity with ETL/ELT pipelines, data warehouses, and integration patterns; hands-on experience with a specific cloud platform (Snowflake, BigQuery, Databricks, Redshift, or similar) is a plus
Familiarity with APIs and file-based data delivery (e.g., S3, SFTP) — enough to follow and contribute to a troubleshooting conversation with engineering partners
Practical understanding of data mapping and normalization concepts — recognizing inconsistent client taxonomies and translating them into clear, structured requirements for engineering — and experience reconciling reported numbers to a client's own source of truth
Understanding of data privacy regulations (GDPR, CCPA) and comfort running several onboardings or workstreams concurrently, each at a different phase
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

Nice To Haves

Familiarity with data visualization tools (Tableau, Looker, Mode)
Hands-on experience with a specific cloud platform (Snowflake, BigQuery, Databricks, Redshift, or similar)

Responsibilities

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
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
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
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
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
Validate outputs against client reporting and vendor exports (e.g. Meta, Google Ads, DV360) — using SQL and dashboards as tools — to catch 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
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
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
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.
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