Data Engineer

Job Description: Data Engineer (Streaming & Integration) Position: Data Engineer (Streaming & Integration) Location: offshore/India Experience: Minimum 8+ years Role Overview We are hiring one Data Engineer to lead the design, development, and operation of streaming and enterprise data pipelines. This role owns technical direction, makes architectural trade-offs, and works directly with onsite clients in the US. This is a builder + owner role. You will write code, review designs, debug production issues, and set engineering standards. Responsibilities • Architect, build, and operate real-time and near-real-time data pipelines • Lead development using Confluent Kafka and Apache Flink o Apache Spark Structured Streaming is acceptable instead of Flink Required Skills & Experience: • 8+ years of hands-on data engineering experience • Exceptional SQL skills (complex analytics, performance tuning) • Strong expertise in Python, Java, or .NET (C#) (at least one OOP language is mandatory) • Deep experience with Confluent Kafka (architecture, partitioning, delivery semantics) • Hands-on streaming experience with: o Apache Flink (preferred) or o Apache Spark Structured Streaming • Strong understanding of: o ETL / ELT patterns o MPP processing concepts o Data modeling and orchestration • Experience with SQL Server • Good understanding of data governance, secure PII handling within streaming data pipelines. • Experience with event serialization formats like Protobuf / JSON Schema Nice to Have • SSIS experience • Docker / Kubernetes • Design and maintain batch and hybrid ETL pipelines using SQL Server and SSIS • Own data quality, observability, and reliability across pipelines • Define and enforce best practices for: o Streaming semantics o Error handling o Backpressure and performance tuning • Work directly with onsite clients: o Translate requirements into technical solutions o Defend design decisions o Troubleshoot complex production issues • Mentor engineers and raise overall engineering bar • Collaborate with DevOps and platform teams in containerized, distributed environments • Manage event schema registries and backward compatibility standards across streaming pipelines. • Partner with downstream and upstream system teams to define robust data contracts and payloads.