Sierra Solutions

Design, develop, and optimize data pipelines and models supporting analytics and operational workflows in life sciences. | Requires 5+ years of experience in data engineering, deep SQL expertise, Oracle familiarity, and experience in life sciences or regulated industries. | Job Summary We are seeking a highly skilled Senior Data Engineer to support a key initiative with our life sciences client. In this role, you will be responsible for designing, building, and optimizing data pipelines that support analytics, reporting, and operational needs across supply chain and manufacturing functions. You'll work closely with technical and business stakeholders to translate existing reporting logic into scalable SQL-based data models and views, ensuring data is accurate, accessible, and production-ready. This is an excellent opportunity for someone who thrives in a fast-paced environment, is deeply knowledgeable in enterprise data systems, and has direct experience supporting data workflows within regulated industries. Primary Responsibilities • Design, develop, and maintain robust data pipelines, data models, and SQL-based transformations to support analytics, reporting, and operational workflows. • Translate existing reporting requirements into optimized SQL views, datasets, and ETL/ELT processes. • Work with cross-functional teams to map, cleanse, and integrate data from Oracle and other enterprise systems into centralized data platforms. • Ensure data quality, reliability, and compliance across all pipeline components—particularly for life sciences manufacturing and supply chain processes. • Collaborate with business analysts and solution architects to align technical solutions with business objectives. • Troubleshoot data pipeline issues and optimize performance for large-scale, complex datasets. • Document data flows, schemas, and transformation logic to support governance and reproducibility. Education and Experience • 5+ years of experience as a Data Engineer, ETL Developer, or similar role. • Deep expertise in SQL with the ability to translate complex logic into efficient SQL views and transformations. • Hands-on experience building data pipelines in modern data engineering environments (ETL/ELT tools, cloud platforms, or on-prem SQL ecosystems). • Strong familiarity with Oracle databases, schemas, and data structures. • Prior experience working within life sciences, specifically supporting supply chain or manufacturing operations. • Solid understanding of data modeling concepts, relational database design, and data integration best practices. • Ability to work independently, communicate effectively, and collaborate with stakeholders across technical and functional teams. • Must be willing and able to work Eastern Standard Time (EST) hours. • Experience with Oracle EBS, OPM data structures, or translating EBS/OPM data into analytical models. • Knowledge of GxP or other life sciences regulatory considerations. • Familiarity with cloud data platforms (AWS, Azure, GCP) or tools like DBT, Snowflake, or Informatica. • Experience supporting reporting or analytics teams using tools like Power BI, Tableau, or similar.

Develop and maintain data QA processes for healthcare data, automate QA using machine learning, validate data integrity, and collaborate with cross-functional teams in an Agile environment. | 5+ years in data quality assurance or analysis with healthcare claims data experience, strong programming skills in Python and SQL, machine learning knowledge, and a relevant bachelor's degree. | Job Summary We are looking for a Senior Data QA Engineer for our Healthcare client to join the Data Science team. The ideal candidate will be crucial in ensuring the accuracy, consistency, and reliability of data products and analytical tools, specifically focusing on healthcare data. As a Senior Data QA Engineer, you will leverage machine learning and other techniques to automate and enhance the data QA process, working closely with data scientists, engineers, and other stakeholders to ensure that our data meets the highest standards. Candidates must have healthcare data experience. Primary Responsibilities • Develop, implement, maintain, and document a comprehensive data QA process and framework. Continuously refine and optimize the data QA process and quickly integrate new data sources. • Create new and maintain existing documentation for testing products. • Define test requirements by leveraging historical data, statistical analysis, and machine learning techniques to ensure accuracy and reliability. • Validate data integrity, consistency, and accuracy across various data sources, databases, and data products. • Conduct data validation and verification processes to ensure compliance with business rules, data definitions, and industry standards. • Perform data profiling and root cause analysis to identify data anomalies, inconsistencies, and quality issues. • Leverage machine learning techniques to automate and enhance the data QA process. • Utilize knowledge of healthcare claims data and outcome measures to ensure high data quality standards. • Proactively assess risks and help to find issues as early as possible. • Stay informed of industry best practices and emerging trends in data QA tools and methodologies. • Guide team members on QA best practices and findings by sharing expertise, providing feedback, and regularly meeting with data scientists and analysts about QA output. • Provide clear and concise feedback to data scientists and other stakeholders on data quality issues, test results, and overall data quality metrics. • Work closely with cross-functional teams to understand data requirements, use cases, and technical specifications. • Actively participate in Agile/Scrum processes, including sprint planning, daily stand-ups, retrospectives, and all stages of the release process—contributing to release planning, coordinating testing and deployment, and ensuring smooth delivery of each release to production. • Identify opportunities for process improvements and contribute to the development of data quality standards and practices. Education and Experience • Experience with healthcare claims data and quality metrics. • Minimum of 5 years of experience in data quality assurance or data analysis. • Proven experience with data validation, data profiling, and root cause analysis. • In-depth knowledge of data warehousing, ETL processes, and data pipeline architectures. • Ability to manage, analyze, and derive insights from large, complex custom datasets. • Experience with programming languages such as Python and SQL for data validation and automation. • Experience with machine learning techniques and tools to automate and enhance QA processes. • Strong analytical and problem-solving skills with a focus on data-driven decision-making. • Excellent verbal and written communication skills with the ability to convey and document complex technical concepts. • Ability to work both independently and in a collaborative environment. • Bachelor's degree in Computer Science, Data Science, Engineering, or a related field.