We are seeking a highly skilled and analytical Data Analytics Engineer to design, build, and optimize analytics solutions that drive data-driven decision-making. The ideal candidate will bridge the gap between raw data and actionable insights by building scalable data models, developing advanced analytics tools, and ensuring data accuracy and accessibility across the organization.
Data Pipeline Development: Build, maintain, and optimize scalable ETL/ELT pipelines to ensure seamless data flow from diverse sources to analytics platforms.
Data Modeling: Design and implement data models that support analytics and reporting needs, ensuring efficient and accurate querying capabilities.
Analytics Solutions: Develop advanced analytics frameworks, dashboards, and visualization tools to translate complex datasets into actionable insights.
Collaboration: Partner with data scientists, business analysts, and stakeholders to understand analytical requirements and deliver tailored solutions.
Data Quality Assurance: Establish and enforce data quality standards to ensure the accuracy, consistency, and reliability of analytics outputs.
Performance Optimization: Monitor and optimize the performance of data pipelines and analytics tools for speed and scalability.
Technology Integration: Integrate data analytics platforms with existing systems and explore new tools to improve data analytics capabilities.
Documentation: Maintain comprehensive documentation for analytics solutions, data models, and workflows.
Bachelor’s degree in Data Science, Computer Science, Information Systems, or a related field (or equivalent experience).
Proficiency in programming languages such as Python, R, or Java for data manipulation and analysis.
Strong SQL skills with experience in working with relational databases and data warehouses like Snowflake, Redshift, BigQuery, or Azure Synapse.
Hands-on experience with data visualization tools such as Tableau, Power BI, or Looker.
Solid understanding of data modeling concepts and best practices.
Familiarity with big data tools like Apache Spark, Hadoop, or Kafka is a plus.
Experience with cloud platforms such as AWS, Azure, or Google Cloud.
Knowledge of statistical methods and data analysis techniques.
Excellent problem-solving skills and attention to detail.
Strong communication and collaboration skills, with the ability to explain technical concepts to non-technical audiences.
Experience with machine learning frameworks and predictive analytics.
Knowledge of Agile or Scrum development methodologies.
Familiarity with data governance and compliance standards.