Role Description 829 is hiring a Senior Analytics Engineer to join our Analytics & AI practice and deliver high-impact BI and data engineering work for our clients. This is a dedicated practice role focused entirely on client engagements, working directly with clients to scope projects, design data architecture, and build solutions that turn raw data into business outcomes. This is a hands-on, individual contributor role with significant client-facing responsibility. You will: - Run discovery, define requirements, design data models, build ETL pipelines, develop dashboards, and present insights to client stakeholders ranging from analysts to C-suite executives. - Own the end-to-end delivery of analytics engagements and serve as the trusted technical advisor to the clients you work with. - Be comfortable writing SQL and dbt models, leading workshops with a client's marketing leadership team, and working through ambiguous data problems independently. This role sits within 829's Analytics & AI practice and reports to the SVP of Analytics & AI. Qualifications - Bachelor's degree in Computer Science, Data Science, Engineering, or a related quantitative field. - 5+ years of experience in BI solution development and implementation, ideally in a consulting or agency environment. - Demonstrated experience leading client-facing engagements and presenting to executive stakeholders. - 3+ years of hands-on Python experience, including data manipulation, automation, and pipeline development. - 4+ years of strong SQL experience, preferably with Snowflake or BigQuery. - 4+ years of experience building dashboards and reports in modern BI tools (Tableau, Sigma, or similar). - Working experience with dbt for data transformation and modeling. - Proven experience designing ETL processes, data models, and BI solution architectures from the ground up. - Knowledge of AI/ML operations and experience maintaining models in production. - Strong problem-solving, communication, and project management skills. - Ability to work independently and deliver results in ambiguous situations. Requirements - Set the technical direction for client engagements and contribute to practice-wide delivery standards. - Design and implement data architectures for clients using Snowflake, BigQuery, dbt, and modern data stack tools. - Build and maintain ETL/ELT pipelines using Python, SQL, and orchestration platforms. - Drive data architecture recommendations and establish best practices across engagements. - Develop and refine reusable templates, frameworks, and assets that improve delivery speed and quality across the practice. - Stay current on tooling, techniques, and AI/ML approaches relevant to client work, and bring those forward into engagements where they create value. Benefits - Remote Workplace: Option to work at our office in Boston or remotely in various states. - Paid Time Off: Generous paid vacation benefits that increase each year, 12 Company Holidays, and Summer Fridays from Memorial Day through Labor Day. - 401K + Match: 401K plan with 4% Safe Harbor employer match after one year of employment. - Life Insurance Benefit: No-cost coverage to ensure peace of mind for your family. - Short Term Disability Benefit: Coverage for illness or injury that keeps you out of work. - Healthcare: Competitive healthcare plans with significant employer coverage. - Commuter Benefits: Pre-tax funds allocation towards your commute. - Continuing Education: Access to webinars, learning platforms, and funding for conferences.

• Model raw data into clean, tested, and reusable datasets. • Since data models are created around business needs, the job of analytics engineers is to define the rules and requirements for the formats and attributes of data. • Translate user and product requirements into data model requirements. • Builds ETL pipelines that can efficiently process very large datasets. • Design, implement and maintain online and offline feature stores to support ML training and inference. • Develop and maintain data and design documentation ensuring all team members are executing against the same architectural vision. • Draft and maintain documents that describe how the data flows from data sources to consumption by visualizing them with directed acyclic graphs (DAGs). • Define metrics and implement tests to guarantee data meets operational and analytics needs. • Responsible for implementing data quality standards across the organization. • Develop and maintain automation, scheduling and monitoring of processes designed to gather data from disparate sources and preparing them for data analysis. • Use CI/CD processes throughout the data model development lifecycle to develop higher quality code and data models without disruption to production.

• Work on building and evolving the organization’s analytical layer, transforming raw data into reliable, documented, and reusable data products. • Be responsible for structuring analytical models, Data Marts, and corporate metrics that support Business Intelligence, Analytics, and Machine Learning initiatives. • Ensure data quality, governance, scalability, and alignment with business needs. • Develop transformations and analytical models using DBT. • Build, maintain, and evolve analytical data layers, including Silver, Curated, and Gold. • Design and implement Data Marts oriented to business area needs. • Translate business rules and processes into scalable, standardized, and reusable data models. • Define, document, and govern corporate metrics, promoting consistency across teams. • Ensure data quality, traceability, observability, and reliability. • Develop automated tests and quality controls to validate data. • Support the provisioning of datasets for consumption by BI, Analytics, and Machine Learning teams. • Collaborate with Data Engineers, Data Scientists, BI Analysts, and business stakeholders. • Promote best practices in analytical modeling, documentation, versioning, and data governance. • Contribute to the evolution of analytical architecture and the organization of data layers. • Participate in defining standards and strategies for consuming corporate data. • Identify opportunities for optimization, performance improvements, and scalability of developed solutions.

Role Description Apartment List runs on data, and the Analytics Engineering team builds the internal data products that power decisions across Product, Marketing, Operations, Finance, and beyond. We hold ourselves to high standards through rigorous process, governance, and technical execution, and we form genuine partnerships with the teams we serve. This role is equal parts strategic thinking and hands-on execution. As a Lead Analytics Engineer, you'll be a technical anchor for the team: - Shaping architecture decisions - Mentoring engineers - Driving initiatives that improve both the health of our data systems and the business outcomes they enable You'll operate at company scope, influencing cross-functional roadmaps and setting the bar for what great analytics engineering looks like at Apartment List. Responsibilities - Design data solutions end-to-end: evaluate architecture options, articulate tradeoffs, and deliver production-ready, testable, maintainable code. - Influence and evolve the team's data architecture standards by researching, testing, and implementing modeling and tooling best practices, and analyzing the technical risks and long-term implications of key decisions. - Proactively find and execute on high-value tech health and business opportunities before they're on anyone's roadmap. - Actively participate in business measurement discussions, communicating both the value and structural limitations of proposed metrics. - Mentor AEs and analysts with targeted feedback and specific growth expectations; enable the broader team through documentation, process development, and knowledge sharing. - Contribute to roadmap and goal-setting as a decision-maker. - Communicate clearly with technical and non-technical audiences, anticipating blockers and scope changes before they require escalation. Qualifications - 7+ years in analytics engineering or a closely related data role, with a track record of end-to-end technical ownership across architecture, delivery, and long-term maintenance. - Expert SQL: performant at scale, structured for readability, and built to handle edge cases. - Advanced dbt, including building team practices around testing, documentation, and modularity. - Proven data architecture decisions with lasting structural impact. - Enabled others through mentorship, code review, process, or tooling. - Easily explain data architecture tradeoffs to both engineers and business stakeholders in the same conversation. - Deep ELT/ETL knowledge with strong instincts for observability and failure recovery. - CI/CD in a data context, including automated testing pipelines and deployment validation. - Bonus: Ownership of BI tools as a product, including semantic layer health and cross-team data model standards. - Bonus: 2+ years working with performance marketing and frontend eventing data. Compensation We carefully consider a variety of factors to determine compensation for each position, including the role, level, and work. The US Total Target Compensation (TTC) for this position is: - Tier 1: $165,000 to $200,000 (Base: $148,500 to $176,000) - Tier 2: $153,000 to $185,000 (Base: $137,700 to $162,800) - Tier 3: $141,000 to $170,000 (Base: $126,900 to $149,600)