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Executive Data Management and Analytics

Full time Express Fourways in Telecommunications
  • Post Date : May 31, 2022
  • Apply Before : June 30, 2022
  • 0 Application(s)
  • View(s) 22
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Job Detail

  • Experience 8 Years +
  • Qualifications Master’s Degree

Job Description

Main Purpose of the Job:

  • The main purpose of the position is to develops strategies and frameworks for, as well as guide and oversee data preparation & exploration, the building of machine learning models, analysing, and reporting, deploying solutions, and the maintenance of them thereafter. The position is accountable for the development and use of data systems by implementing efficient ways to organize, store and analyse data with attention to security and confidentiality across the company, while maintaining data governance and processes, in accordance with all statutory law. The focus will be on; Big data, Machine Learning, Data visualisation, Analytics. Current technologies include; AWS, Informatica stack; Power centre (ETL), DQ, EDC (metadata), Ms SQL, Power BI (on prem), MS SSAS, SSIS.

Create and enforce strategies and policies for effective data analytics and information management:

  • Take ownership and drive the Data Analytics and Information Management Strategy across the company.
  • Develop Data Management programs based on the strategy and the DAMA framework.
  • Design Data Management policies and workshop these policies for approval by the Data Governance Council.
  • Ensure adherence to policies and manage any policy exceptions.
  • Train and support others in the daily use of data systems and ensure adherence to legal and company standards

Data Analytics:

  • Design and implement data engineering, ingestion and curation functions on AWS cloud using AWS native or custom programming.
  • Collaborate with other tech teams to implement advanced analytics algo-rithms that exploit datasets for statistical analysis, inference, prediction, clustering and machine learning.
  • Design, implement and support an analytical data infrastructure providing ad-hoc access to large datasets and computing power, through a reporting interface e.g. Quicksight.
  • Help to continually improve ongoing reporting and analysis processes, automating or simplifying self-service support for customers

Formulate techniques for quality data collection to ensure adequacy, accuracy and legitimacy of data:

  • Determine data that needs to be collected using data modelling.
  • Execute data quality initiatives where data quality measures are negative.
  • Determine resources and systems required for data collection.
  • Interface with other technology teams to extract, transform, and load data from a wide variety of data sources using SQL and AWS big data technologies, including Glue.
  • Design and build out of production data pipelines from ingestion to consumption within a big data architecture, using Java, Python, Scala.
  • Collaborate and work with teams on building out data sets for object recognition and ML models.
  • Create and support of real-time data pipelines built on AWS technologies including Glue, Redshift/Spectrum, Kinesis, EMR and Athena
  • Work closely with team members to drive real-time model implementations for monitoring, alerting and error handing of systems.
  • Establish data collection standards via the data model.
  • Maintain and update the Data Quality measurement framework when required.
  • Ensure data quality standards are met across the business units and track data quality errors.
  • Work closely with Data Stewards for data quality correction

Establish rules and procedures for data sharing with upper management, external stakeholders etc:

  • Determine reporting requirements across the business and develop reports where required
  • Provide mentoring, coaching and subject matter expertise to peers and others in as required for reporting.
  • Understand and assist in database and data store creation.
  • Develop ETL and/or ELT standards and processes for data loading into stores.
  • Assist with and execute on reports and data extraction when needed.

Manage the Data Management Centre of Excellence:

  • Establish central accountability and chart the direction of the CoE based on the Data Strategy.
  • Consolidate reporting outputs across the business.
  • Manage and allocate the budget required to fulfil projects as well as the running of the CoE.
  • Align data and systems infrastructure through artefacts such as data flow diagrams and enterprise relationship diagrams.
  • Defining current known data gaps and challenges.
  • Implementation of projects to address data gaps and challenges.
  • Researching and providing ways of data monetization.

Monitor and analyze information and data systems and evaluate their performance to discover ways of enhancing them (new technologies, upgrades etc.):

  • Gather requirements for data management systems and manage their successful implementation across the business.
  • Provide feedback on performance and usability of data systems to the Data Governance Council.
  • Implement and manage project deliverables for data system deployments.
  • Troubleshoot data-related problems and authorize maintenance or modifications

Self-training and conferences:

  • Keeping up to date with the DAMA DMBOK framework and any changes made to the framework.
  • Ensure company data is compliant based on legislative requirements.
  • Align data requirements and structure to the TM Forum framework.
  • Understanding and implementing new age technology capabilities for company and its subsidiaries such as Big Data, NoSQL databases, Artificial Intelligence etc.

Relationship building:

  • Maintain good working relationships with other departments within the business
  • Demonstrate ability to influence people at a variety of levels internally and externally.
  • Be a thought leader in the business in relation to all Data and Information Management activities.

Working Conditions:

  • Predominantly office work
  • Extended work hours to meet project deliverables often required.

Competencies and Minimum Requirements:

  • Knowledge, qualifications and experience
  • Bachelor’s Degree in Computer Science, Information Technology or other relevant fields – essential
  • Honours or Masters specialising in Cloud computing – desirable.
  • Extensive experience (3 years) in analytics related to working with unstructured datasets – essential
  • Extensive Experience (10 years’ experience) of software engineering best practices across the development lifecycle, including agile methodologies, coding standards, code reviews, source management, build processes, testing, and operations – essential
  • Experience in collaborating and working with cross-functional teams in a dynamic environment – essential
  • Experience and ability to communicate and present complex technical information in a clear and concise manner to a variety of audiences – essential
  • Experience working (3 years) with RESTful API and general service-oriented architectures.
  • Experience working (3 years) with big data and visualization technologies to provide new capabilities and in-crease efficiency
  • Very strong understanding and experience (3 years) in the field of AI and ML related technologies, in relation to designing, building and deploying and operationalising these systems.
  • Experience (2 years) in any of the following AWS platforms: Rekognition, SageMaker, Glue, Athena, Lambda, Glacier, QuickSight, Tensorflow
  • Advanced working SQL knowledge and experience (5 years) working with relational databases, query author-ing (SQL) as well as working familiarity with a variety of databases.
  • Experience building/operating highly available, distributed systems of data extraction, ingestion, and pro-cessing of large data sets
  • Experience working with distributed systems as it pertains to data storage and computing
  • A successful history of manipulating, processing and extracting value from large, disconnected data sets.
  • Experience (5 years) 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, meta data, dependency and workload man-agement.
  • Working knowledge of message queuing, stream processing, and highly scalable Big Data, data stores.
  • Experience with object-oriented/object function scripting languages: Python, Java, C++, Scala, etc.
  • Experience (3 years) in a Data Engineer or similar roles
  • Experience with big data tools: Hadoop, Spark, Kafka, etc.
  • Experience with data pipeline and workflow management tools: Azkaban, Luigi, Airflow, etc.
  • Experience with AWS cloud services: EC2, EMR, RDS, Redshift
  • Experience with stream-processing systems: Storm, Spark-Streaming, etc.
  • Experience in Telecommunications – desirable
  • Experience in Project Management – desirable

Technical Skills:

Analytical Thinking & Problem Solving:

  • Demonstrate excellent analytical rigor and logical thinking with a continuous process improvement mindset. Ability to synthesize information across multiple platforms, systems, and organizations.

General Computer Literacy:

  • Strong working knowledge of MS Office, MS Projects and Visio software packages. Excel and PowerPoint essential.

Business Acumen:

  • Applies commercial insight to deliver a prompt and appropriate response to business matters to influence a positive outcome.

Technical Report Writing:

  • Able to document the procedure adopted and results obtained from a scientific or technical activity or investigation, into a technical report.

Prioritization:

  • Proficient in handling and prioritizing multiple responsibilities and tasks simultaneously

Solution Driven:

  • Identifies problems, analyses problems, generates workable solutions and resolves problems according to acceptable business quality standards and so as to minimise workflow disruption.

Performance driven:

  • Puts in sufficient effort to meet or exceed business and customer expectations both in terms of delivery and quality. Able to act without being prompted to. Demonstrates a sense of urgency, self-motivation and ownership in work. Has a need for achievement. Achieves results.

Perseverance:

  • Is persistent and overcomes obstacles such as physical barriers, criticism, lack of sup-port, discouragement, heavy workloads, customer resistance or tough economic factors.

Attentive to Detail:

  • Thorough. Gives care and consideration to all parts and aspects of tasks. Considers fine points.

Communication Skills:

  • Exchanges information, news, ideas and views to create shared meaning. Communication occurs between levels, departments and employees. Uses appropriate methods of communication and transmits clear, professional messages. Checks own understanding.

Analytical:

  • Systematically examines and evaluates data or information by breaking it into its com-ponent parts to uncover their interrelationships so as to establish trends, changes and to identify risks in order to make meaningful business decisions.

Collaborative:

  • Forms partnerships, joins forces or cooperates with others to jointly work towards a shared goal or purpose.

Conscientious:

  • Sees routine tasks through to completion without prompting.

Time Management:

  • Systematically structures/plans own time and implements this plan. Able to prioritise and action tasks within given timeframes without compromising on quality. Plans include additional time to cater for unforeseen circumstances.

Required skills

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