Senior Big Data Engineer

Project Description

We've been engaged by a large UK Based Investment Bank in the Corporate, Commercial and Institutional Banking (CCIB) potfolio to work on the projects for managing analytics support for all the key initiatives of all segments/products .

Responsibilities

Ensuring quality service output, submitted in a timely manner and communicated effectively

Continuous Improvement in Productivity to the standards prescribed from time to time

Manage data & information support for all the key initiatives across Retail Bank supporting countries & group teams.

Work closely with technology on solutions to resolve identified production issues which are impacting existing infrastructure /solution covering data quality, data assurance, refresh timeliness, data security governance

To work closely with Business & Technology to review the integration of new functionality / technology implementation projects initiated

Extend analytics support spanning across entire gamut of business (Customer Acquisition, Portfolio Management, New Product Development)

Manage analysis & reporting across all Retail products/segments

Interact with metrics owners across various functions and get end to end understanding of the metrics and applicability of the same across various MI dashboards

Provide development opportunities through regular engagement, ensuring that all staff is aware of the opportunities available in order to help them succeed within their role

Create and maintain optimal data pipeline architecture, assemble large, complex data sets that meet functional / non-functional business requirements.

Mandatory Skills

7+ years of relevant technology experience

Hands on Hadoop and Bigdata tech. , HIVE, HBase, Kafka, Spark

Strong analytic skills related to working with unstructured datasets.

Strong experience on SQL

Good Experience on Data Base & Data Ware house building and management

Exposure to data pipelines, architectures and data sets

Experience performing root cause analysis on internal and external data and processes to answer specific business questions and identify opportunities for improvement.

Build processes supporting data transformation, data structures, metadata, dependency and workload management.

Working knowledge of message queuing, stream processing and highly scalable big data data stores.