Tech Lead

Role/ Job Title: Tech Lead Place of Work: Mumbai Roles & Responsibilities: • 'Minimum 7 years of Data Engineering experience and 5 years in large scale Data Lake ecosystem • Clean, prepare and optimise data at scale for ingestion and consumption. • Drive the implementation of new data management projects and re-structure of the current data architecture. • Work with business stakeholders to identify and document high impact business problems and potential solutions. • First-hand experience with the complete software development life cycle including requirement analysis, design, development, deployment, and support. • Advanced understanding of Data Lake/Lakehouse architecture and experience/exposure to Databricks techniques • Work on end-to-end data lifecycle from Data Ingestion, Data Transformation and Data Consumption layer. Versed with API and its usability. • A suitable candidate will also be proficient Scala, Spark, Spark Streaming, AWS, EMR. • He/She should have machine learning experience and experience with big data infrastructure inclusive of MapReduce, Hive, HDFS, YARN, HBase, Oozie, etc. • The candidate will additionally demonstrate substantial experience and a deep knowledge of data mining techniques, relational, and non-relational databases. Secondary Responsibilities: • 'Implement complex automated workflows and routines using workflow scheduling tools. • Build continuous integration, test-driven development and production deployment frameworks. • Excellent oral and written communication skills Learn and use internally available analytic technologies. • Build models at scale using vast amounts of structured and unstructured heterogeneous types of data. • Identify key performance indicators and establish strategies on how to deliver on these key points for analysis solutions Use educational background in data engineering and perform data mining analysis. • Work with BI analysts/engineers to create prototypes, implementing traditional classifiers and determiners, predictive and regressive analysis points. • Engage in the delivery and presentation of solutions Participate in data storage architecture design discussions. • Apply machine learning and/or statistical techniques to time series classification and telemetry anomaly detection problems. Key Success Metrics: 'Ensure timely deliverables. Spot Data fixes. Lead technical aspects of the projects. Error free deliverables