• Develop features and improvements to the HIVERY Curate product in a secure, well-tested, and performant way. • Collaborate with Product and other stakeholders within Engineering to maintain a high bar for quality in a fast-paced, iterative environment. • Solve technical problems of low to moderate scope and complexity. • Write code that meets our internal standards for style, maintainability, and best practices for data engineering in a high scale environment. Maintain and advocate for these standards through code review • Collaborate to identify impediments to our efficiency as a team ("technical debt"), propose and implement solutions. • Represent HIVERY and its values in public communication around specific projects and community contributions. • Ship small features and improvements with guidance and support from senior team members. • Participate on-call rotations

Role Description The Senior Data Engineer will work as a member of the Enterprise Data and Analytics team to support leadership, Operations, Product, Sales, HR, Finance, Legal, and Marketing teams with enterprise data, analytics, and insights. The ideal candidate is experienced in analyzing data, designing and maintaining scalable, high-performance data warehouses, calculating analytics, and creating effective visualizations of metrics. This role requires strong experience across a variety of data architectures, data modeling techniques, data analysis methods, data tools, and data visualization techniques, as well as a proven ability to solve business problems through data-based solutions. The successful candidate must be comfortable working with a wide range of stakeholders and functional teams. Essential Duties and Responsibilities - Directly evaluates, design and maintain centralized enterprise data warehouses, data lakes, and analytics using industry-standard architecture principles and design patterns. - Employ data warehousing best practices to design scalable data models and high-performance processes. - Evaluates and implements efficient methods and tools to extract, transform, and load data from various sources. - Leverage suitable algorithms, statistical methods, and libraries to compute analytics, train data sets, and produce predictive analytics. - Use various optimization techniques to tune data structures and processes for best runtimes to maintain near real-time data refresh. - Create dashboards and reports for data visualization using industry-standard tools and techniques. - Develop readable, clear, easy-to-maintain code, test suites, and baseline results. - Document and communicate analysis, design, development, testing activities, and results effectively. - Participate in and contribute to peer review processes with team members to raise the quality and productivity of the team. - Develop tools and processes to proactively monitor and analyze data quality, data consistency, and performance. - Evaluate and create benchmarks on new technologies, tools, and techniques. - Work with stakeholders throughout the organization to gather requirements and identify opportunities for leveraging data to drive business solutions. Other Responsibilities - Build Relationships: Establish and maintain positive working relationships with others, both internally and externally, to achieve the goals of the organization. - Communicate Effectively: Speak, listen, and write in a clear and thorough manner using appropriate and effective communication tools and techniques. - Foster Teamwork: Work cooperatively and effectively with others to set goals, resolve problems, and make decisions that enhance organizational effectiveness. - Make Decisions: Assess situations to determine importance, urgency, and risks, and make clear decisions which are timely and in the best interests of the organization. - Organize: Set priorities, develop a work schedule, monitor progress toward goals, and track details, data, information, and activities. - Solve Problems: Assess problem situations to identify causes, gather and process relevant information, generate possible solutions, and make recommendations and/or resolve the problem. Qualifications - Bachelor's degree in Computer Science, related field, or equivalent experience. - 10+ years of IT and software development experience with broad exposure to various technical environments and business segments. - 10+ years of experience in database technologies such as MS SQL Server, Oracle, MS Fabric, and Snowflake. - 5+ years of experience creating effective, user-friendly data visualizations using tools such as Power BI, Tableau, OBIEE, or equivalent. - Strong design and problem-solving skills with an emphasis on data warehouse, data quality, and analytics. - Proficiency in data architecture, data model design, procedures, and data quality. - Experience using statistical methods (distributions, regression, etc.) and libraries such as Python to manipulate data and draw insights from large data sets. - Excellent written and verbal communication skills for collaboration and documentation. Other Qualifications - Intrinsically motivated to learn and master new technologies and techniques to grow professionally. Company Description TPx is an Equal Opportunity / Affirmative Action employer. Qualified applicants will receive consideration for employment without regard to race, color, religious creed, sex (including pregnancy, childbirth, breast-feeding and related medical conditions), sexual orientation, gender identity, gender expression, national origin or ancestry, age, mental or physical disability (including medical condition), military or veteran status, political preference, marital status, citizenship, genetic information or other status protected by law or regulation. We are committed to providing reasonable accommodations for qualified individuals with disabilities. If you need assistance or an accommodation, please let us know during the application process. #LI-Remote Req: #26-0081

• Design, build, and maintain data pipeline components spanning ingestion, streaming (Kinesis), storage (S3), and transformation (dbt, Glue). • Lead or contribute to data transformation layer re-architecture a rare opportunity to shape how data flows across an entire suite. • Build and maintain Glue ETL jobs and evolve dbt model layers (staging, intermediate, mart). • Ensure pipeline correctness with exactly-once delivery, deduplication logic, and schema migration management. • Implement data quality assertions and monitor pipeline health proactively. • Partner closely with product managers, analysts, and customer success to translate needs into well-modeled dbt marts.

• CTG is seeking a Senior Data Engineer to design, build, and maintain scalable, efficient data pipelines and systems following modern data engineering best practices. • Design, build, and maintain scalable data pipelines, ETL/ELT workflows, and data models using Python, Apache Spark (PySpark), Databricks, dbt, SQL (PostgreSQL), and AWS Glue. • Develop and optimize AWS-native data platforms leveraging AWS Glue, Amazon EMR, Amazon MWAA (Apache Airflow), Lambda, Step Functions, Amazon S3, Redshift, RDS, DMS, and CloudWatch. • Build high-performance ingestion, transformation, and orchestration workflows for structured and semi-structured data using Apache Iceberg, Parquet, ORC, and Avro. • Design and optimize analytical data platforms using Amazon Athena, Trino, Hive, OpenSearch, and enterprise data catalog technologies. • Integrate enterprise and external data sources across relational and NoSQL platforms including PostgreSQL, Oracle, Redshift, GraphDB, and other NoSQL databases. • Build AI-enabled data solutions using Amazon Bedrock, RAG pipelines, and vector search technologies including Amazon S3 Vector and OpenSearch vector indexes. • Develop cloud infrastructure using CloudFormation (Infrastructure as Code), GitHub, Harness, and enterprise CI/CD pipelines while leveraging SNS, SQS, and EventBridge for event-driven architectures. • Improve the reliability, scalability, performance, and maintainability of enterprise data platforms through monitoring, troubleshooting, automation, and continuous optimization. • Support mission-critical analytics and reporting solutions within large-scale AWS-based federal data environments, implementing solutions that comply with FedRAMP and NIST 800-53 security controls. • Lead modernization initiatives migrating legacy platforms including IBM DataStage, Hadoop, RunDeck, and shell-based workflows to cloud-native AWS services. • Mentor junior engineers through technical guidance, architecture discussions, and code reviews while promoting engineering best practices. • Collaborate with cross-functional teams in an Agile environment to define requirements, deliver high-quality data solutions, and communicate technical concepts effectively to technical and non-technical stakeholders.