Lead Data engineer

Note: Client Interview is must. Mandatory skills: AWS, Databricks, Python, Spark & Pyspark Experience: 6 to 10 years Realtime experience on databricks is must. Collaborate as part of a development team to design and enhance large scale applications developed using Python, Spark & Pyspark . Realtime experience on databricks is must. Evaluates and plans software designs, test results and technical manuals using AWS. Confer with business units and development staff to understand both the business and technical requirements for producing technical solutions. Create and review technical and user-focused documentation for data solutions (data models, data dictionaries, business glossaries, process and data flows, architecture diagrams, etc.). Extend and enhance the business Data Lake. Create or implement solutions for metadata management. Solve for complex data integrations across multiple systems. Design and execute strategies for real-time data analysis and decisioning. Build robust data processing pipelines using AWS Services and integrate with multiple data sources. Analyses and determines data integration needs and follows Agile practices. Job Title: Sr Technical Lead Location: Dallas, TX Mode: Hybrid(3 day a week onsite) C2C or W2 Note: Client Interview is must. Mandatory skills: AWS, Databricks, Python, Spark & Pyspark Key Responsibilities Experience: 6 to 10 years Realtime experience on databricks is must. Collaborate as part of a development team to design and enhance large scale applications developed using Python, Spark & Pyspark . Realtime experience on databricks is must. Evaluates and plans software designs, test results and technical manuals using AWS. Confer with business units and development staff to understand both the business and technical requirements for producing technical solutions. Create and review technical and user-focused documentation for data solutions (data models, data dictionaries, business glossaries, process and data flows, architecture diagrams, etc.). Extend and enhance the business Data Lake. Create or implement solutions for metadata management. Solve for complex data integrations across multiple systems. Design and execute strategies for real-time data analysis and decisioning. Build robust data processing pipelines using AWS Services and integrate with multiple data sources. Translating client user requirements into data flows, data mapping, etc. Analyses and determines data integration needs and follows Agile practices. Required Skills At least 4+ years of experience on designing and developing Data Pipelines for Data Ingestion or Transformation using Scala or Python. At least 4 years of experience with Python, Spark & Pyspark. At least 3 years of experience working on AWS technologies. Experience of designing, building, and deploying production-level data pipelines using tools from AWS Glue, Lamda, Kinesis using databases Aurora and Redshift. Experience with Spark programming (Pyspark or scala). Hands on experience with AWS components like (EMR, S3, Redshift, Lamdba, API Gateway, Kinesis ) in production environments. Strong analytical skills and advanced SQL knowledge, indexing, query optimization techniques. Experience using ETL tools for data ingestion. Experience with Change Data Capture (CDC) technologies and relational databases such as MS SQL, Oracle and DB. Ability to translate data needs into detailed functional and technical designs for development, testing and implementation. #J-18808-Ljbffr