Sr. Data Engineering

Who are we Fulcrum Digital is an agile and next-generation digital accelerating company providing digital transformation and technology services right from ideation to implementation. These services have applicability across a variety of industries, including banking & financial services, insurance, retail, higher education, food, healthcare, and manufacturing. Requirements Role Overview We are looking for an experienced and highly skilled Senior Data Engineer to design, develop, and optimize scalable data platforms and pipelines. The ideal candidate will have strong expertise in PySpark, AWS, and Databricks, with a proven track record of building enterprise-grade data solutions that support analytics, reporting, and machine learning initiatives. This role requires close collaboration with data architects, analysts, data scientists, and business stakeholders to deliver high-quality, reliable, and secure data products. Key Responsibilities Design, build, and maintain scalable batch and real-time data pipelines. Develop robust ETL/ELT workflows using PySpark and Databricks. Implement and manage cloud-native data solutions on AWS. Optimize data processing performance, scalability, and cost efficiency. Build and maintain data lakes, data warehouses, and lakehouse architectures. Ensure data quality, governance, security, and compliance standards are met. Collaborate with cross-functional teams to understand business requirements and translate them into technical solutions. Automate deployment and monitoring using CI/CD and infrastructure-as-code practices. Troubleshoot and resolve production data issues and pipeline failures. Mentor junior engineers and contribute to engineering best practices. Required Skills & Qualifications Bachelor’s or Master’s degree in Computer Science, Engineering, Information Systems, or related field. 6+ years of experience in Data Engineering or Big Data development. Strong hands-on experience with: PySpark AWS services (S3, Glue, EMR, Lambda, Redshift, IAM, CloudWatch, etc.) Databricks Strong SQL and data modeling skills. Experience with distributed data processing and large-scale datasets. Expertise in building ETL/ELT pipelines and orchestration frameworks. Experience with workflow orchestration tools such as Airflow. Strong understanding of data lake/lakehouse architecture. Experience with Git, CI/CD pipelines, and DevOps practices. Knowledge of performance tuning and optimization techniques in Spark. Excellent problem-solving and communication skills. Preferred Qualifications Experience with Delta Lake and Databricks optimization techniques. Knowledge of streaming technologies such as Kafka or Spark Streaming. Exposure to Terraform or CloudFormation. Experience working in Agile/Scrum environments. AWS or Databricks certifications are a plus. Key Competencies Strong analytical and troubleshooting skills Ownership and accountability Collaboration and stakeholder management Attention to detail Ability to work in fast-paced environments