Lead Data Engineer

Lead Data Engineer

Responsibilities :

• Extensive experience on building Data Pipeline by scrapping data from Multiple Data Source (CSV, Excel, JSON etc).
• Sound knowledge on building the Data Pipeline from scalability point of view by understanding the Application Landscape. Analyse structural requirements and builds data pipeline and models for analytics applications
• Strong Knowledge and working experience on ETL concept. Proficiency in data warehousing design, tuning and ETL/ELT process development
• Collect, collate data from disparate data sources such as RDBMS systems, logs, data files of various formats. Strong SQL and data model understanding is necessary.
• Cleanse the data, treat and process data for correctness, uniformity, fitment for further analytical processing. Should work with the large and diverse volumes of enterprise datasets.
• Designs and builds conceptual and logical data models and flowcharts which conform to existing standards and conventions
• Good to have Knowledge in data modelling, process modelling, master data management, metadata management and enterprise data management.
• Experience in migrating data from legacy applications.
• Should be good in communication to create data stories and explain satirical outcomes. Should be able work with Notebooks and EDA components to explain analytical outcomes in a convincing way supported by data and visualizations.
• Knowledge of data mining and segmentation techniques
• Proficiency in database structure principles and Relational DBMS with 3rd normal form relational modelling and dimensional modelling
• Should independently work with global teams, deliver analytical and predictive outcomes expected. Also keep looking for opportunities to mine additional use cases as domain and data understanding grows.
• An Aspirant with good to have experience on AI/ML use cases with appropriate python/R packages. Deep dive into business domain and domain data, extract features, do through feature engineering so that interactive and iterative ML models can be built.
• Evaluation and recommendation of new data management and storage technologies/standards

• Partners with security specialists to ensure compliance with data security and privacy mandates
• Builds and revises data dictionary definitions, governance practices, and standards.

Expertise in following Technical areas is MUST.

• Knowledge of programing languages: Python and SQL.
• Knowledge on Big Data Analytics (PySpark)
• Expertise in working with modern data lakes and data warehouses.
• Strong knowledge on ETL Process and Technique.
• Familiar with Cloud Solution : Azure (Data Bricks, Data Factory, ADLS )
• Deployment Knowledge: CI/CD Tool Chain.
• Strong knowledge on SQL DB and NoSQL DB.
• Exposure on Data science modelling concept.
  

Expertise in following Technical areas is Good to have.

• Adobe and Google Analytics concepts
• Knowledge in various machine leaning frameworks like Tensorflow , Keras and PyTorch .
• Exposure to MS Power Bi
• Knowledge on R and Scala
• Experience in the field of Automotive Sales and Aftersales, Oil and Gas Industry, Banking and Finance, Retail (FMCG) and e-Commerce.

Other Skills

• Extremely good Analytical and Problem Solving Skill
• In-depth understanding of SDLC life Cycle
• Experience in Agile Product Development methodology
• Experience in Converting business requirements in business documents
• Good communication skills
• Experience in the field of Automotive Sales and Aftersales, Oil and Gas Industry, Banking and Finance, Retail (FMCG) and e-Commerce is good to have.

Organization

Primary Location

Work Locations