Director of Technical Data Implementation

National Debt Relief, LLC.•,

1d•$193,500 - $222,500•Remote

About The Position

As the Director of Technical Data Implementation, you will serve as a senior technical leader within National Debt Relief's (NDR) Data, Analytics & AI (DAI) organization, accountable for the data elements of platforms that capture, generate, or enable analysis of business data — their integrations, in-tool data configurations, and data integrity. Business product leaders own the overall implementation, rollout, and operational use of these platforms (including partner or user onboarding, migrations from prior systems, and ongoing business administration); this role owns the data-layer implementation within and across them. This role ensures those platforms are configured correctly across three dimensions: data flowing in via integrations and capture, data flowing out via reports and downstream integrations, and in-tool data configurations. You will also uphold data governance, compliance, and data quality within these platforms and at their integration boundaries — complementing, but distinct from, enterprise data governance applied at the data warehouse and BI layer. Initial focus is on the full implementation of the enterprise Customer Data Platform (CDP) in partnership with marketing product leadership, and the new digital A/B testing platform integrated with Snowflake, with scope expanding over time to additional analytics-supporting platforms across the business.

Requirements

Bachelor's degree in Computer Science, Data Engineering, Information Systems, or related field required.
10 years in data integration, marketing technology, analytics engineering, or a related technical discipline, with a focus on system configuration and data quality.
Experience operating as a senior technical leader working across engineering and business teams.
Computer competency and ability to work with a computer.
Prioritize multiple tasks and projects simultaneously.
Exceptional written and verbal communication skills.
Punctuality expected, ready to report to work on a consistent basis.
Attain and maintain high performance expectations on a monthly basis.
Work in a fast-paced, high-volume setting.
Use and navigate multiple computer systems with exceptional multi-tasking skills.
Remain calm and professional during difficult discussions.
Take constructive feedback.
Deep technical fluency in how enterprise systems produce, capture, and move data, including APIs, event streams, webhooks, batch exports, and ETL/ELT pipelines.
Working familiarity with cloud data warehouse environments (Snowflake preferred) — sufficient to specify requirements, validate landed data, and partner with Data Engineering.
Working knowledge of the platform categories that comprise a modern data and analytics stack, including CRMs (Salesforce), customer data platforms, web and product analytics tools, experimentation and measurement platforms, and partner or channel platforms.
Strong command of data quality concepts (completeness, accuracy, consistency, timeliness) and the ability to diagnose issues across source systems, pipelines, and data models and engage the right owners for resolution.
Working knowledge of data governance and compliance within business platforms, including sensitive or regulated data, access controls, and retention; familiarity with the consumer financial services regulatory landscape is a plus.
Experience defining and enforcing data contracts, event schemas, and integration standards across multiple business platforms and partner teams.
Hands-on proficiency configuring data-centric platforms through their native UIs, including event setup, field mapping, and integration configuration.
Working proficiency in SQL for data validation and investigation.
Strong communication skills, with the ability to explain data quality concepts to both business stakeholders and technical engineers.
Strong project management and cross-functional coordination skills, with a track record of delivering system-to-system integrations through partnership with engineering and business teams.

Nice To Haves

Experience in financial services industry preferred.
Familiarity with the consumer financial services regulatory landscape is a plus.

Responsibilities

Spearhead the implementation of system-to-system data integrations across data-centric platforms.
Own the data implementation project plan for each in-scope platform, including scope, sequencing, dependencies, and timelines.
Partner with department leaders, Product & Engineering (Salesforce, communications platforms, and other engineering-owned systems), the Digital team (website and mobile app), Data Engineering (data warehouse and BI platforms), and Data Science (ML models) to deliver these integrations end to end.
Deliver implementation work through the relevant technical partner team where one exists; perform the work directly within system UIs where one does not.
Advise on external implementation engagements as needed, including scoping and SOW input, and project-manage their technical delivery — driving requirements, timelines, and outcomes.
Define and enforce data contracts for inbound and outbound flows of each platform, including field definitions, data types, expected values, refresh cadence, and completeness requirements.
Establish data standards, naming conventions, and validation rules applied within data-centric platforms and at their integration points.
Implement controls for sensitive and regulated data, access permissions, and retention configurations within platforms and across their data flows.
Review system configurations, integration designs, and event schemas during setup to address data requirements prior to go-live.
Identify and resolve data quality issues at the source, including misconfigured events, missing fields, broken mappings, and incorrect values.
Partner with Analytics Engineering and reporting teams to trace data issues from business reports back to source, remediate at the root, and ensure data models reflect accurate source data.
Maintain documentation of system configurations, data mappings, and integration specifications.
Establish scalable processes for data quality monitoring, issue triage, and resolution across the ecosystem.
Engage on critical data incidents affecting these platforms during business hours; this role is not on-call outside business hours, though may be asked to participate in response to high-severity incidents on an exception basis.