Role Description Data is the fuel behind Appfire's global footprint. We’ve grown fast, and now our focus has shifted toward building the most mature, high-performing data platform in our industry. We are looking for a Senior Data Engineer to take ownership of our core infrastructure, champion engineering excellence, and help shape the future of our data ecosystem. Leveraging your expert Python, SQL, and cloud infrastructure skills, you will develop pragmatic architectures that ensure stability and performance across the organization. As a key member of our central data insights team, you will evolve our custom data lake ("firelake" built on AWS and Snowflake). To accelerate our platform optimization efforts, you will ruthlessly eliminate technical debt, resolve edge cases, and introduce modern, AI-driven development practices. This isn't just about maintaining pipelines; it's about architecting a highly secure, resilient, and scalable foundation that empowers company-wide decision-making. If you love solving complex data puzzles, driving overarching data strategy, and seeing the direct impact of your work across an entire organization, let's talk. What you’ll do: - Data Delivery - Solution Lifecycle Ownership: Own the complete solution development lifecycle, from driving initial requirements to building Proof of Concepts (POCs) and Minimum Viable Products (MVPs), and continuously iterating to improve and scale production solutions. - Data Delivery & Pipelines: Work closely with internal and external partners to stand up and optimize robust data delivery solutions. Build, manage, and scale complex data pipelines (ETL and reverse ETL) utilizing expert-level Python and Airflow. - Custom Integrations: Develop standard integration patterns as well as custom data pipelines that interface with atypical APIs to efficiently extend our data delivery capabilities. - Data Platform & Infrastructure - Pragmatic Architecture: Architect and maintain highly efficient, cost-effective scalable data solutions and cloud infrastructure from scratch. - Platform Evolution: Be the key driver in the evolution of our custom data lake ("firelake" built on AWS/Snowflake), serving as the ultimate subject matter expert on our tech stack and data infrastructure. - Ingestion & Data Quality: Manage Fivetran and custom pipeline operations, proactively handle schema drift, and implement rigorous data quality checks to ensure we have ingested a true and accurate representation of all source systems. - AI & Modern Engineering - AI-Driven Efficiencies: Spearhead AI adoption in our daily engineering workflows to automate Pull Requests, enforce new "definition of done" standards, and accelerate CI/CD, test coverage, and release management. - AI Implementation: Drive the integration of AI capabilities directly into our data solutions by interfacing with LLMs and developing intelligent assistants and agents to solve complex business problems. - Continuous Innovation: Stay on the forefront of the rapidly evolving data landscape and drive proof-of-concepts for new tools to ensure we leverage best-in-class technologies to scale quickly. - Data Governance & Security - PII & Security Management: Implement advanced PII management by leveraging automated data classification techniques, applying masking policies, and enforcing sophisticated row-level and column-level security practices. - Proactive Governance: Develop and deploy robust monitoring solutions and governance strategies to ensure accurate, secure data is available on time and to the correct audience. - Leadership & Collaboration - End-to-End Ownership: Autonomously lead 3-5 concurrent cross-departmental data projects, driving everything from requirements gathering and UAT to production deployment. - Strategic Communication: Partner seamlessly with business, analytics, and engineering teams, translating complex technical architectures into relatable concepts to influence stakeholders and align goals. - Engineering Excellence & Mentorship: Champion best practices, systematically reduce technical debt, write clear documentation, and provide both technical and soft-skill mentorship to junior team members. Qualifications - Engineering Background: 5+ years in a data or software engineering role with a deep understanding of the full data lifecycle, modern data warehousing, and agile software development best practices. - Proven Delivery: 5+ years of experience autonomously building scalable data products, pipelines, and solutions that support company-wide systems and overarching business goals. - Project Leadership: Exceptional personal organization and multitasking skills, with the ability to work with minimal supervision while driving 3-5 concurrent cross-departmental projects. Requirements - Data Warehousing & SQL: 5+ years of advanced SQL optimization and complex ETL/ELT pipeline development, with extensive, hands-on experience in Snowflake. - Advanced Python & Development: 5+ years of advanced Python programming. - Cloud Infrastructure & IaC: 5+ years of experience designing and maintaining AWS cloud environments. Strong, hands-on proficiency with Terraform and Docker is required. - Orchestration & Ingestion: Deep operational experience with Airflow for orchestration, dbt (3+ years) for transformation, and familiarity with managing Fivetran pipelines. Benefits - Equity: every Appfire team member is eligible for company equity, fostering a genuine sense of ownership and shared success as we continue to grow. - Paid Time Off: Unlimited Paid Time Off (PTO) and 10 paid company holidays annually. - Health & Wellness Benefits: Comprehensive benefits package including: - Medical insurance fully covered by Appfire (100% employer-paid) - Dental insurance with costs shared 50/50 between Appfire and employees - Vision insurance - Group Term Life Insurance - Voluntary Life Insurance - Critical Illness coverage - Accident Insurance - Long-Term Disability coverage - Employee Assistance Program (EAP) - Fitness and wellness reimbursements through our benefits provider. - Learning & Development: Grow your skills and advance your career through Appfire University, our custom on-demand learning platform designed to support continuous professional development. - Community Impact & Volunteering: Every employee receives three fully paid volunteer days per year to participate in our Corporate Social Responsibility (CSR) initiatives and support local communities. - Additional Benefits: Flexible Spending Accounts (FSA), monthly remote-work stipend, dedicated Amazon account for ordering approved equipment and peripherals, competitive 401(k) plan with company matching. - Flexibility: This role is fully remote and can be performed anywhere within the United States.

• Define the enterprise data architecture: Own the conceptual, logical, and physical data models for MeridianLink's analytical and operational data platform, including source-aligned, integrated, and consumption-ready layers. • Build the meta-model: Design and maintain a meta-model that captures entities, relationships, business definitions, ownership, lineage, sensitivity classifications, and SLAs — and make sure it is wired into our tooling, not stuck in a slide deck. • Drive the lakehouse strategy: Architect our medallion (bronze / silver / gold) Delta Lake patterns on Databricks; define standards for partitioning, clustering, schema evolution, slowly changing dimensions, and historical reproducibility. • Be hands-on: Write PySpark, SQL, and Delta Lake code. Build reference implementations, prototype patterns, review pull requests, and personally model critical domains rather than delegating every detail. • Lead data integration design: Set patterns for ingestion through Informatica Data Management Cloud (IDMC) and direct Databricks pipelines, including CDC, batch, streaming, and API-based sourcing from our SaaS products and third-party systems. • Champion data governance and lineage: Partner with data governance, security, and compliance leaders to operationalize cataloging, lineage, classification, masking, and access controls across the platform (Unity Catalog, IDMC, and adjacent tools). • Standardize data modeling practices: Establish the standards, naming conventions, and review processes used by the Data Engineering team. Coach engineers on dimensional modeling, Data Vault, and other techniques where they best fit the use case. • Partner across the business: Work closely with Product, Engineering, Analytics, ML, Finance, Risk, and Customer-facing teams to translate business needs into durable data designs. • Influence the roadmap: Identify gaps in tooling, capability, and skill; propose investments; and drive multi-quarter initiatives that materially improve how MeridianLink uses its data.

• Stand up and operate per-client data ingestion (ODBC, API, and file sources) into the warehouse via our ELT layer • Run rigorous row-count and parity checks to verify raw landings directly against the client’s source systems • Own end-to-end pipeline operations across concurrent clients, directly diagnosing freshness issues, failure alerts, infrastructure costs, and incidents • Build and maintain isolated, three-layer dbt projects (staging → intermediate → marts) for each client assignment • Construct robust fact and baseline models that reproduce a client’s exact source-of-truth numbers, producing reconciliation documentation for client sign-off • Perform engineering standards rigor by applying version-controlled, tested, peer-reviewed, and reproducible data practices utilizing a disciplined local-to-CI workflow • Apply right-size engineering rigor appropriately for a startup environment, partnering cleanly with platform engineering without gold-plating solutions • Sit directly with client operators, controllers, and analysts to pull essential domain logic and uncover system patterns • Translate discovery conversations directly into clear metric definitions within the semantic layer and queryable business marts • Own core business-rule tests and metric definitions (Cube.dev) powering executive dashboards and natural-language AI querying • Construct the quality framework using automated dbt tests, anomaly checks, freshness monitoring, and PII awareness • Optimize and structure context-rich datasets with clean joins and clear descriptions so AI agents can reason correctly via the signal-mcp tool server • Ensure all ongoing data operations capture structured traces that continuously feed our cross-client intelligence layers • Collaborate and train other team members • Perform other duties as required by the role

• Develop and test automation for data aggregation and consolidation • Participate in technical decisions together with the tech lead and the team.