Data Engineer

As a Data Engineer specializing in Python and PySpark, you will be responsible for designing, building, and maintaining scalable ETL pipelines. Your key responsibilities will include: - Designing, building, and maintaining scalable ETL pipelines using Python and PySpark. - Ensuring data quality, reliability, and governance across systems and pipelines. - Collaborating with cross-functional teams to understand business requirements and translate them into technical solutions. - Performing performance tuning and troubleshooting of big data applications. - Working with large datasets from multiple sources and preparing them for analytics and reporting. - Following best practices in coding, testing, and deployment for enterprise-grade data applications. To qualify for this role, you should have: - 3-10 years of professional experience in data engineering, preferably in utility, energy, or related industries. - Strong proficiency in Python programming. - Hands-on experience with PySpark for big data processing. - Good understanding and working knowledge of Palantir Foundry. - Experience with SQL and handling large datasets. - Familiarity with data governance, data security, and compliance requirements in enterprise environments. - Strong problem-solving and analytical skills. - Excellent communication and collaboration skills. Nice-to-have skills include experience with cloud data services (AWS, Azure, GCP), knowledge of utilities domain data models and workflows, exposure to DevOps/CI-CD pipelines, familiarity with visualization tools like Tableau, Power BI, or Palantir dashboards, and experience in Palantir Foundry. In this role, you will have the opportunity to work on cutting-edge Palantir Foundry-based solutions in the utilities sector, be part of a dynamic, collaborative, and innovation-driven team, and grow your technical expertise across modern data platforms.,