Big Data Engineer

As a Big Data Engineer with 5 to 8 years of experience, your role will involve leading the end-to-end analysis of large-scale multisource datasets using Big Data technologies to drive insights for strategic and operational decision-making. You will design, develop, and optimize complex analytical queries using Starburst Trino Presto for fast federated access across enterprise data sources. Additionally, you will build, manage, and optimize analytics workflows in Databricks using SQL, PySpark, and Python for scalable data processing. Your key responsibilities will include: - Partnering closely with Data Engineering, Architecture, and Platform teams to influence data models, analytics architecture, and performance optimization - Translating ambiguous business problems into structured analytical approaches and clearly communicating findings, trends, and recommendations to senior stakeholders - Defining and enforcing best practices for data quality validation, reconciliation, and governance within analytics solutions - Mentoring junior and mid-level analysts by providing technical guidance, code reviews, and analytical best practices - Supporting executive reporting dashboards and advanced ad-hoc analysis as required - Contributing to the continuous improvement of analytics processes, standards, and reusable frameworks Qualifications required for this role include: - 7-10 years of experience as a Data Analyst or Senior Data Analyst in large-scale enterprise data environments - Strong hands-on experience with Big Data ecosystems such as Hadoop, Hive, and Spark distributed data platforms - Proven experience using Starburst Trino/Presto for complex, high-performance analytical queries - Extensive experience with Databricks, including Databricks SQL, Spark-based analytics, and notebook development - Expert-level SQL skills with query optimization, performance tuning, and complex joins - Strong proficiency in Python and/or PySpark for data analysis and transformation - Solid understanding of data warehousing, data modeling, and analytics architecture concepts - Excellent analytical, problem-solving, and stakeholder communication skills - Experience working on cloud platforms such as AWS, Azure, or GCP - Exposure to BI and visualization tools like Power BI, Tableau, or equivalent - Knowledge of enterprise data governance, metadata management, and data quality frameworks - Experience working in Agile DevOps delivery models with a strong ownership and ability to lead quality outcomes end-to-end - Clear communication and stakeholder management skills - Mentoring mindset and collaboration across QA, Dev, and DevOps teams Please note that the company values continuous improvement of analytics processes, standards, and reusable frameworks, and encourages a collaborative and mentoring culture across various teams. As a Big Data Engineer with 5 to 8 years of experience, your role will involve leading the end-to-end analysis of large-scale multisource datasets using Big Data technologies to drive insights for strategic and operational decision-making. You will design, develop, and optimize complex analytical queries using Starburst Trino Presto for fast federated access across enterprise data sources. Additionally, you will build, manage, and optimize analytics workflows in Databricks using SQL, PySpark, and Python for scalable data processing. Your key responsibilities will include: - Partnering closely with Data Engineering, Architecture, and Platform teams to influence data models, analytics architecture, and performance optimization - Translating ambiguous business problems into structured analytical approaches and clearly communicating findings, trends, and recommendations to senior stakeholders - Defining and enforcing best practices for data quality validation, reconciliation, and governance within analytics solutions - Mentoring junior and mid-level analysts by providing technical guidance, code reviews, and analytical best practices - Supporting executive reporting dashboards and advanced ad-hoc analysis as required - Contributing to the continuous improvement of analytics processes, standards, and reusable frameworks Qualifications required for this role include: - 7-10 years of experience as a Data Analyst or Senior Data Analyst in large-scale enterprise data environments - Strong hands-on experience with Big Data ecosystems such as Hadoop, Hive, and Spark distributed data platforms - Proven experience using Starburst Trino/Presto for complex, high-performance analytical queries - Extensive experience with Databricks, including Databricks SQL, Spark-based analytics, and notebook development - Expert-level SQL skills with query optimization, performance tuning, and complex joins - Strong proficiency in Python and/or PySpark for data analysis and transformation - Solid understanding of data warehousing, data modeling, and analytics architecture concepts - Excellent analytical, problem-solving, and stakeholder co