Lead Data Engineer- Databricks + Apache Pinot

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. About the role We are looking for a Data Engineering Lead with 10-14 years of experience to drive solution architecture, client engagements, pre-sales activities, and technical leadership. The ideal candidate should have deep expertise in Databricks, Apache Pinot, Spark, Kafka, Python, and SQL, along with exceptional communication and stakeholder management skills. Key Responsibilities Participate in pre-sales discussions, solutioning workshops, and client stakeholder meetings. Lead client POCs, technical assessments, and architecture discovery sessions. Design and implement scalable end-to-end Data & Analytics solutions on Databricks. Architect and build batch and real-time data pipelines using Spark, Kafka, and Python. Design and implement Lakehouse architectures, Delta Lake, and Medallion frameworks. Drive Apache Pinot-based real-time analytics and low-latency data solutions. Establish data governance, security, and best practices using Unity Catalog. Define and implement CI/CD and DevOps best practices for data platforms. Perform architecture reviews, code reviews, performance tuning, and cost optimization. Work closely with business stakeholders, delivery teams, and client leadership. Mentor and guide Data Engineers and Technical Leads, fostering technical excellence and capability development across the team. Provide hands-on technical leadership during design, development, and production support activities. Required Skills Databricks (Expert Level) Apache Spark / PySpark Python SQL Apache Kafka Apache Pinot Delta Lake & Lakehouse Architecture Unity Catalog CI/CD & DevOps Data Modeling & Performance Optimization Experience 10-14 years of overall experience in Data Engineering. 5+ years of hands-on Databricks experience. 3+ years of participating in pre-sales calls/ architecture level discussion. Strong experience in client-facing roles, pre-sales, solutioning, and stakeholder management. Proven track record in building enterprise-scale data platforms and mentoring technical teams. Soft Skills Excellent verbal and written communication skills. Strong presentation and stakeholder management abilities. Ability to engage confidently with client executives, business leaders, and technical teams. Proven leadership and team mentoring experience. Expereince - 10 years to 14 years relevant expereince - 5 years to 8 years Requirements Key Responsibilities Participate in pre-sales discussions, solutioning workshops, and client stakeholder meetings. Lead client POCs, technical assessments, and architecture discovery sessions. Design and implement scalable end-to-end Data & Analytics solutions on Databricks. Architect and build batch and real-time data pipelines using Spark, Kafka, and Python. Design and implement Lakehouse architectures, Delta Lake, and Medallion frameworks. Drive Apache Pinot-based real-time analytics and low-latency data solutions. Establish data governance, security, and best practices using Unity Catalog. Define and implement CI/CD and DevOps best practices for data platforms. Perform architecture reviews, code reviews, performance tuning, and cost optimization. Work closely with business stakeholders, delivery teams, and client leadership. Mentor and guide Data Engineers and Technical Leads, fostering technical excellence and capability development across the team. Provide hands-on technical leadership during design, development, and production support activities.