> Markdown version of [/jobs/ext/4225885-lead-data-engineer](https://www.wearedevelopers.com/jobs/ext/4225885-lead-data-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Lead Data Engineer - **Company:** OPEN CONVERSATION LLC - **Location:** Atlanta, GA, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Java (Programming Language), Amazon Web Services, Data Analysis, Batch Processing, Business Intelligence Development, Code Review, Data as a Services, Information Engineering, Data Governance, Persistent Data Structure, Data Systems, Data Warehousing, Relational Databases, Database Queries, Dimensional Modeling, Distributed Data Store, Distributed Systems, Fault Tolerance, Python (Programming Language), PostgreSQL, Meta-Data Management, Data Streaming, Data Logging, Data Processing, Data Ingestion, AWS Lambda, Event Driven Architecture, Data Lakes, Pyspark, Low Latency, Apache Flink, AWS Glue, Qlikview, Apache Kafka, Stream Processing, Data Pipelines, Amazon Redshift - **Published:** October 6, 2026 - **Apply:** https://www.careerjet.com/jobad/usee34bb180f23120a21f627579027f385 ## About the Role We are seeking a Senior Data Engineer to design, build, and maintain scalable data engineering solutions that power analytics, reporting, and data products across the organization. This role works closely with product managers, data analysts, BI developers, and other engineers to deliver reliable, high quality data pipelines across both batch and real time streaming architectures. The ideal candidate has deep experience building cloud native data solutions on AWS, strong fundamentals in distributed systems, and hands on experience with stream processing frameworks such as Apache Flink, using Python and Java., * Amazon Redshift * Data warehousing concepts (dimensional modeling, star/snowflake schemas) * Experience building or supporting enterprise data warehouses or data lakes * Familiarity with event driven architectures and real time analytics use cases * Experience with data governance, metadata management, or lineage tools, * 5+ years of experience in data engineering or backend engineering * Strong hands on experience with Python and Java in distributed systems * Experience building streaming data applications using Apache Flink and Kafka * Solid experience with AWS data services * Strong SQL skills and understanding of relational data modeling * Experience working with large scale, distributed data systems ## Description * Design, develop, and maintain scalable data pipelines supporting batch and real time streaming workloads * Build and optimize data processing jobs using AWS Glue, PySpark, Python, and Java * Develop and maintain stream processing applications using Apache Flink * Implement reliable data ingestion solutions using AWS DMS, Kafka, and AWS Lambda * Design and manage data persistence layers using Amazon Aurora (PostgreSQL) and related AWS services Streaming & Batch Processing * Design and support real time and near real time streaming pipelines using Kafka and Apache Flink * Build and maintain stateful stream processing jobs, including windowing, aggregation, and event time processing * Develop efficient batch processing workflows for large scale data transformation and enrichment * Ensure data consistency, latency, fault tolerance, and reliability across streaming and batch systems Data Quality, Reliability & Performance * Implement monitoring, logging, and alerting for batch and streaming data pipelines * Diagnose and resolve data quality, performance, and scalability issues * Apply best practices for schema evolution, checkpointing, fault tolerance, and back pressure handling * Optimize pipelines for performance and cost efficiency Analytics & Consumption * Enable downstream analytics and reporting through well modeled, well documented datasets * Partner with analytics and BI teams supporting tools such as Qlik * Support data consumers by improving data discoverability, usability, and trust Collaboration & Engineering Excellence * Collaborate with product, analytics, and platform teams to translate requirements into technical solutions * Participate in architecture discussions, design reviews, and code reviews * Contribute to data engineering standards, reusable frameworks, and documentation * Mentor junior engineers and promote best practices across the team Core Technologies * AWS Glue * PySpark * Python * Java * Apache Flink (Stream Processing) * Kafka * AWS DMS * AWS Lambda * Amazon Aurora (PostgreSQL) * Streaming and Batch Data Processing * Qlik (analytics / BI consumption)