Sr. Technical Implementations Specialist (US)

Measured•Austin, TX

10h•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 Role is responsible for onboarding new clients, ensuring a fully connected, loaded, and QA'd data foundation. This involves owning the entire onboarding journey from source connections through historical ingestion, normalization, and output QA. The Senior Implementation Specialist will manage discrete workstreams within large enterprise engagements and handle lower-complexity client onboardings end-to-end. This role requires direct engagement with client marketing and data teams to translate requirements, acting as a consultant on data preparation to meet model requirements, and understanding the product and models to make informed tradeoffs. The ideal candidate is a seasoned, 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 using best-in-class technology, including AI, and expects team members to evolve with these tools, maintaining 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).
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).
Familiarity with APIs and file-based data delivery (e.g., S3, SFTP) — enough to follow and contribute to a troubleshooting conversation with engineering partners.

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 workstreams, driving them to committed go-live dates while managing several concurrent onboardings.
Surface scope, data-quality, and timeline risks early with recommendations.
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.
Distinguish firm requirements from acceptable tradeoffs, make and defend those calls, and push back on requests that compromise model integrity.
Judge whether a client data limitation is a genuine blocker or manageable caveat and set expectations before go-live.
Scope required feeds, determine the right ingestion method, and stand up connections across various methods (APIs, file-based feeds, warehouse shares, direct integrations).
Land the full history each feed requires and prove completeness against the client's reporting.
Diagnose ingestion failures, partner with client-side engineers and vendors to resolve them, and establish monitoring.
Author and maintain specification layers mapping raw client data into Measured's normalized model.
Assess every spec change for downstream impact before it goes live, balancing client-specific accuracy against reusable patterns.
Document mapping logic and reasoning behind judgment calls.
Own QA for workstreams, ensuring data ties out against client source-of-truth, vendor exports, and upstream feed tables.
Validate outputs against client reporting and vendor exports using SQL and dashboards.
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.
Independently lead client-facing working sessions.
Manage client expectations on timelines and dependencies with transparency.
Gather and synthesize client feedback to inform product and onboarding improvements.
Develop deep expertise in Measured's data pipelines, normalization layers, and platform architecture.
Understand incrementality testing and media mix modeling to guide data preparation.
Stay current on product roadmap and platform changes affecting client data preparation.
Serve as the internal reference point for data-requirements questions.
Contribute to technical documentation, implementation runbooks, and internal training materials.
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 client and feed knowledge to reduce dependency on individuals.
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.
Build reusable templates, spec patterns, and validation queries.
Improve quality and scalability by catching requirements gaps early and surfacing cross-client issues.
Partner with leadership on strategic initiatives like data health monitoring, proactive alerting, QA frameworks, and applying AI approaches.
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.
Work with Product to communicate client pain points, feature requests, and usability feedback.
File and shepherd well-specified internal requests.
Contribute to cross-functional projects that improve onboarding experience and platform reliability.