About the job
At SunnyData, our mission is to empower our clients throughout their data journey, enabling them to drive their business success. We firmly believe that a strong foundational framework is essential for achieving exceptional data-driven outcomes, encompassing highly scalable architectures, resilient data engineering pipelines, straightforward data consumption layers, and precise Machine Learning (ML) and Artificial Intelligence (AI) applications. As a Senior Data Engineer, you will be pivotal in enhancing this customer journey. You will collaborate closely with internal teams and clients to conceptualize, construct, and implement data solutions that capture, analyze, transform, and leverage data to facilitate insights in AI, ML, and Business Intelligence.
Your Impact
Join a dedicated team committed to assisting new and existing clients with their data engineering requirements.
Guide clients in making optimal technical decisions to achieve their objectives.
Engage with multiple client accounts, tracking and reporting their progress effectively.
Design, develop, and operationalize complex data solutions, resolving issues, applying transformations, and recommending data cleansing and quality improvement strategies.
Evaluate data sources to assess their value and advocate for their inclusion in analytical workflows.
Implement core data management practices encompassing data governance, data security, and data quality.
Collaborate across teams to facilitate delivery and educate end users on data products and analytic environments.
Conduct data and systems analysis, assessment, and resolution for defects and incidents of moderate complexity, addressing issues as needed.
Test data movement, transformation code, and data components to ensure reliability.
Qualifications
A minimum of 5 years of relevant experience in data engineering and data product development.
Proficient in one or more of the following:
Data Engineering technologies (e.g., Spark, Hadoop, Kafka)
Data Science and Machine Learning technologies (e.g., pandas, scikit-learn, Hyperparameter Optimization)
Data Warehousing tools (e.g., SQL, OLTP/OLAP/DSS)
Strong comprehension of the complete data analytics workflow.
Proven track record of domain expertise with the ability to understand technical concepts and apply them effectively.
