Role Description We’re looking for a Solutions Engineer to join our Data Management team, reporting to the Director of Implementation Services. You’ll help implement the Redpoint Customer Engagement Hub (CEH) for our clients, focusing on the ETL, data hygiene, and identity resolution components that power our Redpoint Data Management (RPDM) product. This is a strong opportunity for an engineer early in their career who wants to grow into a subject matter expert across our Marketing/Campaign product suite. What You'll Do - Deploy Redpoint software based on client-approved Business Requirements and Design documents - Build ETL processes to consume 1st- and 3rd-party data into the marketing data repository using Redpoint Data Management - Develop into a subject matter expert in Redpoint Interaction, our Marketing/Campaign orchestration product - Collaborate with development team members and project leadership to implement solutions that meet project standards, escalating issues and challenges as needed - Provide support, troubleshooting, and forensic assistance for client implementations, both Redpoint-hosted and client-hosted - Collaborate with development teams to identify new product features and functionality - Support recruiting of qualified personnel as needed Qualifications - 2–5 years of experience in a solutions engineering, data engineering, or implementation role - Strong problem-solving skills and the ability to work effectively in a team environment - Strong client interaction and communication skills, both written and verbal - Experience building automations that consolidate disparate data sources into a single operational or OLAP environment - Working knowledge of relational database design and data manipulation concepts, including data hygiene and match/merge techniques supporting identity resolution and householding - Demonstrated knowledge of SQL - Experience with ETL/ELT and 3rd-party data integration technologies (e.g., Azure Data Factory, Fivetran, Matillion, Alteryx, Informatica) - Ability to travel as required Nice to Have - Experience with Snowflake or other cloud data warehouse platforms - Familiarity with AI/ML-powered data tools or generative AI applications in a marketing or data context - Java and familiarity with web services technologies (SOAP, JSON) - Cloud-based hosting experience (Microsoft Azure, AWS) - Marketing Campaign Management or Customer Engagement/CRM experience, especially in retail - Tableau or other BI tool experience Benefits - Competitive Medical, Dental, and Vision Insurance - Flexible leave policy – take the time you need, when you need it - 10 company-paid holidays Salary Range $85-110K Requirements Must be able to work for any US employer – Redpoint is not able to offer sponsorship at this time.

Role Description The Engineer I is a technical resource on a platform/capability team responsible for supporting the day-to-day design, implementation, and operational activities of the team. - Assisting the team with processing transactions, resolving issues, and administering platform or capability systems, solutions, and services. - Maintaining code repositories and Git-based services, if applicable. - Writing code, leveraging third-party services, configuring systems, and implementing solutions in an agile way. - Collaborating with team to identify technical solutions for business and enterprise needs. - Developing technical knowledge and expertise related to domain area systems, solutions, services and applications. Qualifications - Bachelor’s degree (or equivalent experience) in Information Technology, Computer Science, Engineering, or a related field. - 1+ years of experience developing in Python and SQL, with a strong foundation and a commitment to continuously improving programming expertise. - Working knowledge of data management principles, data integration processes, and cloud-native technologies. - Familiarity with DevOps practices and Git-based version control for collaborative development. - Foundational understanding of APIs, with the ability and curiosity to quickly expand knowledge. - Basic exposure to object-oriented and/or ontology-based data environments (e.g., graph databases, semantic models, or platforms like Salesforce). - Collaborative, growth-oriented team player who brings new ideas, demonstrates strong problem-solving skills, and is eager to learn, experiment, and contribute within a high-performing team environment. Requirements - Regular and reliable job attendance. - Effective verbal and written communication skills. - Exhibit respect and understanding of others to maintain professional relationships. - Independent judgement in evaluation options to make sound decisions. - Home office environment with the ability to work effectively surrounded by moderate home environment noise - (Telework). Benefits - Competitive compensation and benefits package including a 401(k). - 100% company-funded pension plan. - Health, dental, and vision plans. - Life insurance. - Paid time off to include company and individual holidays, vacation, sick, caregiving, and parental leave. - Performance-based and peer-based recognition. - Tuition reimbursement.

• Design, develop, and maintain scalable ETL/ELT pipelines using Python, SQL, and AWS services • Design and evolve layered Data Lake architectures following the Medallion Architecture pattern • Build solutions for ingesting, processing, and integrating structured, semi-structured, and unstructured data • Apply Artificial Intelligence, Machine Learning, or Large Language Models (LLMs) to classify, standardize, validate, and enrich data • Collaborate with software engineers, data analysts, business stakeholders, and clients to translate business requirements into scalable technical solutions

• You will own and evolve our data pipelines on GCP — building new ones, hardening existing ones, improving data quality, and making clean, trustworthy data available across the organisation. • Designing ELT/ETL processes on BigQuery and ClickHouse, building real-time pipelines on Pub/Sub and Kafka with Dataflow (and where it fits, Flink/Spark). • Orchestrating workflows with Airflow, and ensuring data is properly cleaned, modelled, and served for analytics, ML training, and online inference. • Partnering with ML engineers on feature pipelines, monitoring data drift, and keeping models well-fed and retrained as needed. • Consuming and building REST APIs, integrate with third-party SaaS sources, and treating infrastructure as code. • Collaborating closely with Infrastructure, Software Engineering, Product, and ML/AI engineers. • Helping drive the migration off Snowflake onto GCP-native stack — and retire shadow pipelines along the way. • Owning the orchestration layer in Airflow, including SLAs, retries, and data quality gates. • Modeling data for analytics and for ML — including feature pipelines that serve both training and low-latency online inference. • Capturing requirements from stakeholders and translating them into pragmatic, well-scoped data products. • Continuously improving data quality, reliability, observability, and cost efficiency. • Identifying new data sources worth acquiring and integrating them cleanly.