Data Analytics Engineer

Data Engineer Our client is seeking a Data Engineer to take ownership of end-to-end data processes within a growing, values-driven organization. This individual will play a key role in ensuring data is accurate, reliable, and actionable across the business. The ideal candidate is hands-on, detail-oriented, and comfortable working across the full data lifecycle—from ingestion and transformation to reporting and stakeholder enablement.Data Engineer ResponsibilitiesOwn data quality across systems by identifying, troubleshooting, and resolving inconsistencies and inaccuracies.Design, build, and maintain scalable ETL/ELT pipelines to transform raw data into clean, structured datasets.Manage data ingestion processes from multiple internal and external sources, including APIs and databases.Develop and optimize SQL queries, data models, and schemas to support analytics and reporting needs.Create and maintain dashboards and reports in Power BI, ensuring they are accurate, user-friendly, and actionable.Partner with business stakeholders to translate requirements into data solutions and meaningful insights.Monitor pipeline performance and reliability, proactively addressing failures and inefficiencies.Contribute to data architecture design, including data lake structure and best practices.Document data sources, transformations, and workflows to support transparency and scalability.Collaborate cross-functionally with engineering and business teams to support data-driven decision making.Data Engineer Qualifications~3+ years of experience in a data engineering, analytics engineering, or similar role with ownership of data pipelines and reporting.~ Strong proficiency in SQL, including complex queries, joins, and performance optimization.~ Hands-on experience with Python for data transformation, scripting, and automation.~ Proven experience building and maintaining Power BI dashboards, including data modeling and DAX.~ Familiarity with cloud-based data platforms, preferably within a Microsoft ecosystem (e.g., Azure Data Factory, Synapse, or similar tools).~ Experience working with data lakes and modern data architecture concepts.~ Ability to work with APIs and semi-structured data formats such as JSON.~ Strong communication skills with the ability to explain data concepts to non-technical stakeholders.~ Detail-oriented with a strong sense of ownership and accountability for data accuracy.Experience with ERP or CRM systems as data sources (e.g., Microsoft Dynamics environments).Familiarity with transformation frameworks such as dbt.Background supporting financial or operational data where accuracy is critical.