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