Role Description The Senior Manager, Data Engineering leads a team of data engineers responsible for designing, building, and operating the scalable data pipelines, architectures, and platforms that power AssistRx’s data-driven initiatives. This role blends hands-on technical leadership with people management, owning delivery across multiple concurrent client implementations and internal data programs. Partnering closely with product, analytics, engineering, and business stakeholders, the Senior Manager translates strategic priorities into executable engineering roadmaps, develops talent, and ensures the team consistently delivers reliable, high-quality, and compliant data solutions. This role is accountable for both the technical health of the data platform and the growth and performance of the engineering team. Qualifications - Bachelor’s degree in Computer Science, Engineering, Mathematics, or related field required; advanced degree a plus. - 8–12+ years of experience in data engineering or data architecture, including 2+ years leading or managing engineers. - Advanced proficiency in SQL and data modeling, with strong architectural judgment. - Strong experience with ETL/ELT tools (e.g., Azure Data Factory, dbt, Informatica, Talend). - Hands-on experience with cloud data platforms (e.g., Snowflake, Azure, AWS). - Proven track record delivering large-scale data warehouses, data lakes, and BI solutions. - Familiarity with big data technologies (e.g., Spark, Hadoop ecosystem). - Experience working with healthcare data, including PHI/PII, preferred. - Demonstrated people leadership, project delivery, and stakeholder management skills. - Excellent communication, collaboration, and organizational skills. Requirements - Lead, mentor, and develop a team of data engineers, setting clear goals, expectations, and growth plans. - Conduct performance reviews, provide ongoing coaching, and support career development across the team. - Manage resourcing, capacity planning, and workload balancing across multiple projects and clients. - Foster a collaborative, inclusive, and high-performing engineering culture. - Own end-to-end delivery of data engineering initiatives, ensuring quality, timeliness, and alignment with business goals. - Plan, prioritize, and track team workstreams across client onboarding, migrations, and platform enhancements. - Remove blockers, manage risks and dependencies, and escalate issues appropriately to leadership. - Partner with project and program managers to set realistic timelines and commitments. - Provide technical direction for ETL/ELT development using Azure Data Factory, dbt, Snowflake, Salesforce, and related platforms. - Review and guide data architecture, models, warehouses, data lakes, and data vault designs for scalability and maintainability. - Ensure adoption of engineering best practices for integration, transformation, testing, and deployment. - Make build-versus-buy and tooling recommendations in partnership with the Director. - Ensure robust data quality frameworks, validation, and monitoring are implemented across pipelines. - Uphold data accuracy, integrity, security, and compliance, including PHI/PII considerations. - Establish standards and documentation for data flows, architecture, and transformations. - Drive resolution of performance, reliability, and cost-efficiency issues across the platform. - Partner with BI, analytics, product, and business teams to align data solutions with reporting and consumption needs. - Communicate progress, risks, and technical concepts clearly to both technical and non-technical stakeholders. - Contribute to roadmap planning and prioritization of data initiatives across the organization. - Identify and implement improvements to engineering processes, automation, and operational efficiency. - Evaluate emerging tools, technologies, and frameworks to advance team capability. - Stay current with industry trends in data engineering, cloud platforms, and big data technologies. - Perform other related duties as assigned by leadership. Benefits - Supportive, progressive, fast-paced environment - Competitive pay structure - Matching 401(k) with immediate vesting - Medical, dental, vision, life, & short-term disability insurance

• Design and deliver product data products on Databricks — owning ingestion, transformation, and serving layers across the medallion architecture (bronze/silver/gold) to produce certified datasets consumed across product, analytics, and the wider business • Lead the hard problems in the domain — event schema governance across multiple products, identity resolution and sessionisation, high-volume event pipelines, and modelling the path from product usage to engagement and learner outcomes • Define and evolve engineering standards — dbt patterns, event ingestion patterns, data contracts, testing, observability — and contribute to cross-cutting standards across the wider data function • Own data quality and contracts for the data products you ship — implementing quality checks, maintaining contracts as the interface between producers and consumers, and ensuring issues are caught early and remediated cleanly • Raise the technical bar around you — through code review, design input, pairing, and the kind of senior IC presence that lifts the engineers and analysts you work with • Translate operational complexity — multiple product event sources, schema drift, ongoing integration of acquired products — into clean, durable engineering execution the business can rely on

• Design and deliver enterprise data products on Databricks - owning ingestion, transformation, and serving layers across the medallion architecture (bronze/silver/gold) to produce certified datasets consumed across the business • Lead the hard problems in the domain - entity resolution, master data, integration of acquired systems, and the engineering complexity of operating across multiple ERP and CRM platforms • Define and evolve engineering standards - dbt patterns, ingestion patterns, data contracts, testing, observability - and contribute to cross-cutting standards across the wider data function • Own data quality and contracts for the data products you ship - implementing quality checks, maintaining contracts as the interface between producers and consumers, and ensuring issues are caught early and remediated cleanly • Raise the technical bar around you - through code review, design input, pairing, and the kind of senior IC presence that lifts the engineers and analysts you work with • Translate operational complexity - multi-system landscapes, ongoing integrations, fragmented source systems - into clean, durable engineering execution that the business can rely on

• Own and drive the design, development, and deployment of end-to-end data solutions and pipelines for enterprise clients. • Translate business requirements into technical data architectures, integration patterns, and models that align with phData methodologies, standards, and best practices. • Ensure solutions meet performance, security, reliability, and scalability requirements in production environments. • Ensure engagements are delivered on time, within scope, and with measurable business value for clients. • Collaborate with cross-functional partners including data engineering, software engineering, analytics, and platform teams to deliver successful client engagements. • Provide technical leadership during design and code reviews, proof-of-concepts, and implementation workshops. • Ensure high quality in deliverables through testing, documentation, and adherence to governance and change management processes. • Partner with practice and account leaders to identify opportunities to expand engagements, improve delivery, and standardize reusable patterns and components. • Contribute to internal initiatives such as developing accelerators, templates, playbooks, and best practices for cloud data platforms. • Create clear technical documentation, including architectures, roadmaps, and operational runbooks, that can be reused across engagements. • Share knowledge with peers through mentoring, informal coaching, and internal communities of practice. • Represent phData with professionalism in all interactions, communicating clearly with both technical and non-technical stakeholders.