Role Description We are looking for a Data Engineer who enjoys solving complex problems and building scalable data solutions. This person will play a key role in designing, developing, and optimizing data platforms that support analytics, reporting, machine learning, AI initiatives, and other data-driven business needs. The ideal candidate is comfortable working on new projects, contributing to technical architecture decisions, and collaborating with cross-functional teams to deliver end-to-end solutions. Strong communication skills are important, as you'll need to explain technical concepts to both technical and non-technical audiences and work closely with clients, stakeholders, and team members. Essential Functions: - Assemble large, complex data sets that meet business requirements through extraction, transformation, and loading of data from a wide variety of data sources. - Provide operational support and troubleshooting for existing processes and systems. - Work closely with architects, solution leads, data owners, Data Scientists and key stakeholders to facilitate and coordinate the data platform backlog grooming process, triaging new feature requests in preparation for future project activities. - Deliver automation & efficient processes to ensure high quality throughput & performance of the entire data & analytics platform. - Ensure data extraction, transformation and loading data meet data security & compliance requirements. - Engage with data source platform leads to gain tactical and strategic understanding of data sources required by Agency Data Services AI/ML as well as Data Office standards. - Create data tools for data scientist team members that assist them in building and optimizing models. Qualifications - BS degree in Computer Science, Data Science, Engineering, or equivalent software/services experience required. - 4+ years working with SQL, Snowflake, Databricks, Spark, and other big data technologies. - 4+ years using Python, SQL, PySpark, R, or similar languages and manipulating, processing, and extracting value from large, disconnected data sets. - 4+ years building and optimizing data pipelines, architectures, and data sets to answer business questions and identify opportunities for improvement. - 2+ years supporting large-scale data processing and storage using Azure Data Factory, Integration Runtime, Data Lake, Databricks, Spark, Azure ML, and Cosmos DB. - 2+ years addressing privacy, compliance, and security aspects of data storage and processing; and delivering data solutions in Agile environments. - 2+ years with software development and CI/CD methodologies and tools for automated infrastructure code and MLOps and designing, implementing, and maintaining automation platforms and tools, including Ansible Tower, Azure, ARM, Terraform Enterprise, Azure DevOps, and GitHub Actions. - 2+ years with Salesforce FSC and Salesforce Data Cloud. Requirements - Strong communication and problem-solving skills. - Troubleshooting expertise. - Proficiency in Python, SQL. - Experience with Extract, Transform, Load (ETL) processes. - Experience building data pipelines. - Experience with Apache Spark and Apache Hadoop. - Experience with Amazon Web Services. Benefits - Health insurance - Paid holidays - Flexible time off - 401k retirement savings plan and company match with pre-tax and ROTH options - Dental insurance - Vision insurance - Employer paid disability insurance - Life insurance and AD&D insurance - Employee assistance program - Flexible spending accounts - Health savings account with employer contributions - Accident, critical illness, hospital indemnity, and legal assistance - Adoption assistance - Domestic partner coverage

• Design and implement ingestion and transformation pipelines from industrial source systems into clean, auditable datasets • Work directly with data from MES, SCADA, historians, PLC/OT systems, LIMS/LAB/OPS platforms, and other operational sources • Perform data quality audits and identify: Anomalies, Dead or inactive signals, Data gaps, Inconsistencies • Develop datasets and validation logic supporting KPI definitions and PoC delivery • Build PoC components such as: Batch analytics pipelines, Event detection logic, Time-series transformations • Create lightweight PoC tooling, dashboards, applications, or visualizations when required • Support presales and consulting teams by shaping technical solutions and identifying business value hidden in industrial data • Produce delivery-grade documentation, handover materials, and implementation support assets

• Perform strategic leadership and oversight of data management staff • Direct performance, security, and scalability of enterprise data environments • Establish strategic roadmap for database and data warehouse services • Establish governance structures, policies, and standards for data management and architecture • Collaborate with senior leadership to align data initiatives with business objectives • Oversee administration of enterprise databases

Role Description - Design, develop, and maintain ETL pipelines using DBT and other data integration tools. - Collaborate with data engineers, analysts, and business teams to define requirements and deliver data solutions. - Build and optimize data models (star/snowflake schemas, fact/dimension tables) to support reporting and analytics. - Write, test, and document DBT models and macros for data transformation. - Implement data quality checks, testing, and validation frameworks within DBT. - Optimize query performance and troubleshoot pipeline bottlenecks. - Manage version control (Git), deployment, and CI/CD pipelines for DBT projects. - Ensure compliance with data governance, security, and privacy standards. - Stay updated with the latest modern data stack technologies (Snowflake, BigQuery, Redshift, Airflow, Fivetran, etc.). Qualifications - Proven experience as an ETL Developer / Data Engineer with hands-on DBT expertise. - Strong knowledge of SQL and relational databases (Snowflake, Redshift, BigQuery, PostgreSQL, etc.). - Experience in cloud data warehouses and modern ELT/ETL frameworks. - Proficiency in data modeling (Kimball/Inmon methodologies). - Familiarity with version control (Git), CI/CD pipelines, and collaborative workflows. - Understanding of data governance, security, and quality best practices. - Strong problem-solving skills with the ability to optimize and debug pipelines.