Senior Data Engineer

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    "ExternalDescriptionStr": "<p><span><b>Role: AWS Data Engineer</b></span></p>\n<p><br><span>We are looking for a <b>Data Engineer</b> with strong skills in <b>Python and PySpark</b> to design and build data solutions for a Fortune 500 client. The role focuses on building <b>data pipelines and integrations on AWS</b> as part of an enterprise data lake platform.</span></p>\n<p><span>&nbsp;</span></p>\n<p><span><b>Key Responsibilities</b></span></p>\n<ul>\n <li><span>Build and test <b>data processing applications using PySpark and Python</b></span></li>\n <li><span>Develop <b>data pipelines using AWS Glue ETL or EMR</b></span></li>\n <li><span>Create <b>AWS Lambda functions</b> using Python (pandas, json, requests, awswrangler)</span></li>\n <li><span>Work with data from:</span>\n  <ul>\n   <li><span>Relational databases (Oracle)</span></li>\n   <li><span>NoSQL databases (DynamoDB, MongoDB)</span></li>\n   <li><span>File systems (S3, HDFS)</span></li>\n  </ul></li>\n <li><span>Implement <b>event-driven pipelines using Kafka</b></span></li>\n <li><span>Develop <b>REST APIs using FastAPI or Flask</b></span></li>\n <li><span>Implement basic <b>authentication (OAuth2 / JWT) for APIs</b></span></li>\n <li><span>Build <b>workflow orchestration pipelines</b> using Step Functions and EventBridge</span></li>\n <li><span>Work with <b>big data file formats</b> such as Parquet, Avro, ORC, JSON</span></li>\n <li><span>Optimize Spark jobs using standard techniques (partitioning, joins, etc.)</span></li>\n <li><span>Use <b>Glue Crawlers</b> to catalog datasets</span></li>\n <li><span>Monitor and troubleshoot jobs using <b>CloudWatch</b></span></li>\n <li><span>Support deployment using <b>Docker containers</b></span></li>\n</ul>\n<p><span>&nbsp;</span></p>\n<p><span><b>Required Skills</b></span></p>\n<ul>\n <li><span>Hands-on experience with <b>Python and PySpark</b></span></li>\n <li><span>Experience with AWS services:</span>\n  <ul>\n   <li><span>S3, Glue, Lambda, EMR, Step Functions, EventBridge, Athena</span></li>\n  </ul></li>\n <li><span>Experience with <b>Kafka integration</b></span></li>\n <li><span>Strong SQL skills (writing complex queries)</span></li>\n <li><span>Experience working with <b>data file formats</b> (Parquet, Avro, ORC, JSON, XML)</span></li>\n <li><span>Experience using Python libraries (pandas, requests, boto3)</span></li>\n <li><span>Experience building <b>REST APIs (FastAPI or Flask)</b></span></li>\n</ul>\n<p><span><b>&nbsp;</b></span></p>\n<p><span><b>Experience</b></span></p>\n<ul>\n <li><span><b>4+ years</b> of experience in Data Engineering or related field</span></li>\n <li><span>Bachelor’s degree in Computer Science or related field (or equivalent)</span></li>\n</ul>\n<p>Base Compensation Range: $130,000 – $150,000</p>\n<p>&nbsp;</p>\n<p>The posted range is the hiring range for this role — a subset of the broader range available to employees over time — and reflects base salary across our national hiring scale. Final offers are based on several factors, including the candidate's skills and experience, internal pay equity, work location, market conditions for the role, and the specific scope and responsibilities of the position. The top of the range is reserved for candidates who notably exceed the requirements; the lower end applies to those with less experience or fewer preferred qualifications. For positions based in higher-cost zones (e.g., California, New York, New Jersey), actual compensation may exceed the posted range; your recruiter will share specifics during the process.</p>",
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