Data Engineering Toolkit

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Demonstrate up to date expertise in Data Engineering practices and provides solutions for the identification, acquisition, cleansing, profiling, and ETL (extracting, transformation, and loading) of data used in data sciencE Discovery and deployment solutions.

More Uses of the Data Engineering Toolkit:

  • Collaborate with the Data Engineering team to optimize Data Model and architecture to reduce Data Storage duplication, optimize ETL processes, and query performance.

  • Manage Data Engineering and product to ensure proper analytics tracking is set up (ideally before launch) for all new features/products and supporting in development of etls.

  • Manage work with Data Engineering and Data Platform teams to conduct ongoing performance enhancement by troubleshooting and introducing new technologies.

  • Ensure you brief; build production grade models on large scale datasets to optimize Marketing Performance by utilizing advanced Statistical Modeling, Machine Learning, or Data Mining techniques and marketing science research.

  • Contribute to Shared Data Engineering tooling and standards to improve the productivity and quality of output for Data Engineers across your organization.

  • Make sure that your planning complies; monitors Industry Trends in Data Infrastructure, Data Architecture and Data Engineering; Assesses, develops and implements data Integration Tools.

  • Provide guidance and mentorship to managers and individual contributors on the high quality Data Engineering and infrastructure Engineering teams.

  • Secure that your strategy complies; designs (and defines) the Data Engineering Best Practices to be implemented as a repeatable process for Data ingestion, cleansing, wrangling, and features generation needed for Data Science.

  • Devise: design and execute analytic projects in collaboration with business, product, Data Engineering, finance, Business Analysts, and other specialists.

  • Evaluate: partner with the analytics and Data Engineering teams to understand problems to be solved with your current Data Platform, identify solutions, and deliver high quality data foundations to data stakeholders across the business.

  • Be certain that your organization develops partnerships and work closely with IT execution teams on the development of analytic infrastructures, Data Engineering, or related Business Intelligence enablement efforts.

  • Be accountable for confirming that new analytics adhere to data and platform guidelines created in partnership with platform architects, Data Governance, Data Engineering, and other relevant teams.

  • Ensure you aid; understand and proactively communicate factors affecting Business Performance to stakeholders by partnering with Business Leaders, other analysts and Data Engineering teams.

  • Head: partner with Key Stakeholders to identify initiatives and execute solutions to people related business problems using Data Analysis, Advanced Analytics and Data Engineering Best Practices.

  • Manage work with Product Management, platform engineering, Cloud Infrastructure, and Data Engineering teams to find the optimal way to scale applications and the infrastructure.

  • Ensure you commit; lead Business Intelligence, Digital Technology and delivery, Emerging Technologies, Enterprise Applications systems, Data Science, Data Engineering.

  • Control: partner with your Data Engineering team to build requirements for Data Infrastructure necessary to facilitate efficient analysis and reporting.

  • Manage work with Data Engineering and Product Teams to ensure proper data tracking is set up for features and products, and support debugging and development of new ETL processes.

  • Translate business/User Requirements into Technical Requirements, and applies creative Problem Solving that bring requirements to fruition for a team.

  • Standardize: Data Science and Data Engineering skill set focused on providing consultative services and conducting development work for information/Data Management solutions.

  • Steer: work closely with the Engineering teams throughout the development process in ensuring Best Practices and technical soundness (scalability, reliability, performance, security) for Data Engineering.

  • Become an advocate for the Data Engineering team by developing and championing Data Engineering practices with the team and with your organization at large.

  • Confirm your enterprise understands Data Analytics, complex Event Processing, Data Warehouses, Big Data technologies, Data Engineering, Data Science and Data Visualization.

  • Become the expert in one of the domains of API design, payments processing, risk/fraud detection, Data Engineering, Machine Learning, or blockchains.

  • Confirm your enterprise ensures common Data Model design and maintenance, Data Distribution, consolidation, and integration compliance and Data Engineering and Data Engineering Best Practices.

  • Supervise: strategically partners and work with it execution teams on the development of analytic infrastructures, Data Engineering, or related Business Intelligence efforts.

  • Provide technology agnostic technical leadership, DRIVE Technology stack selection and ensure the Project Team is setup for success on any number of Open Source, commercial, on premise and/or cloud based Data Engineering technologies.

  • Be accountable for developing curated datasets, partnering with Data Engineering, and applying a variety of Statistical Techniques, explore, evaluate and ultimately select features to drive supervised and unsupervised model designs.

  • Investigate data and monitor Data Quality partner closely with and provide requirements to the Data Engineering teams that can be clearly acted upon.


Save time, empower your teams and effectively upgrade your processes with access to this practical Data Engineering Toolkit and guide. Address common challenges with best-practice templates, step-by-step Work Plans and maturity diagnostics for any Data Engineering related project.

Download the Toolkit and in Three Steps you will be guided from idea to implementation results.

The Toolkit contains the following practical and powerful enablers with new and updated Data Engineering specific requirements:

STEP 1: Get your bearings

Start with...

  • The latest quick edition of the Data Engineering Self Assessment book in PDF containing 49 requirements to perform a quickscan, get an overview and share with stakeholders.

Organized in a Data Driven improvement cycle RDMAICS (Recognize, Define, Measure, Analyze, Improve, Control and Sustain), check the…

  • Example pre-filled Self-Assessment Excel Dashboard to get familiar with results generation

Then find your goals...

STEP 2: Set concrete goals, tasks, dates and numbers you can track

Featuring 999 new and updated case-based questions, organized into seven core areas of Process Design, this Self-Assessment will help you identify areas in which Data Engineering improvements can be made.

Examples; 10 of the 999 standard requirements:

  1. What will be the consequences to the stakeholder (financial, reputation etc) if Data Engineering does not go ahead or fails to deliver the objectives?

  2. How do your controls stack up?

  3. How is Change Control managed?

  4. What are the types and number of measures to use?

  5. Does Data Engineering create potential expectations in other areas that need to be recognized and considered?

  6. What are the barriers to increased Data Engineering production?

  7. Have all basic functions of Data Engineering been defined?

  8. What are the processes for audit reporting and management?

  9. What are the success criteria that will indicate that Data Engineering objectives have been met and the benefits delivered?

  10. What threat is Data Engineering addressing?

Complete the self assessment, on your own or with a team in a workshop setting. Use the workbook together with the self assessment requirements spreadsheet:

  • The workbook is the latest in-depth complete edition of the Data Engineering book in PDF containing 994 requirements, which criteria correspond to the criteria in...

Your Data Engineering self-assessment dashboard which gives you your dynamically prioritized projects-ready tool and shows your organization exactly what to do next:

  • The Self-Assessment Excel Dashboard; with the Data Engineering Self-Assessment and Scorecard you will develop a clear picture of which Data Engineering areas need attention, which requirements you should focus on and who will be responsible for them:

    • Shows your organization instant insight in areas for improvement: Auto generates reports, radar chart for maturity assessment, insights per process and participant and bespoke, ready to use, RACI Matrix
    • Gives you a professional Dashboard to guide and perform a thorough Data Engineering Self-Assessment
    • Is secure: Ensures offline Data Protection of your Self-Assessment results
    • Dynamically prioritized projects-ready RACI Matrix shows your organization exactly what to do next:


STEP 3: Implement, Track, follow up and revise strategy

The outcomes of STEP 2, the self assessment, are the inputs for STEP 3; Start and manage Data Engineering projects with the 62 implementation resources:

Examples; 10 of the check box criteria:

  1. Cost Management Plan: Eac -estimate at completion, what is the total job expected to cost?

  2. Activity Cost Estimates: In which phase of the Acquisition Process cycle does source qualifications reside?

  3. Project Scope Statement: Will all Data Engineering project issues be unconditionally tracked through the Issue Resolution process?

  4. Closing Process Group: Did the Data Engineering Project Team have enough people to execute the Data Engineering Project Plan?

  5. Source Selection Criteria: What are the guidelines regarding award without considerations?

  6. Scope Management Plan: Are Corrective Actions taken when actual results are substantially different from detailed Data Engineering Project Plan (variances)?

  7. Initiating Process Group: During which stage of Risk planning are risks prioritized based on probability and impact?

  8. Cost Management Plan: Is your organization certified as a supplier, wholesaler, regular dealer, or manufacturer of corresponding products/supplies?

  9. Procurement Audit: Was a formal review of tenders received undertaken?

  10. Activity Cost Estimates: What procedures are put in place regarding bidding and cost comparisons, if any?

Step-by-step and complete Data Engineering Project Management Forms and Templates including check box criteria and templates.

1.0 Initiating Process Group:

2.0 Planning Process Group:

3.0 Executing Process Group:

  • 3.1 Team Member Status Report
  • 3.2 Change Request
  • 3.3 Change Log
  • 3.4 Decision Log
  • 3.5 Quality Audit
  • 3.6 Team Directory
  • 3.7 Team Operating Agreement
  • 3.8 Team Performance Assessment
  • 3.9 Team Member Performance Assessment
  • 3.10 Issue Log

4.0 Monitoring and Controlling Process Group:

  • 4.1 Data Engineering project Performance Report
  • 4.2 Variance Analysis
  • 4.3 Earned Value Status
  • 4.4 Risk Audit
  • 4.5 Contractor Status Report
  • 4.6 Formal Acceptance

5.0 Closing Process Group:

  • 5.1 Procurement Audit
  • 5.2 Contract Close-Out
  • 5.3 Data Engineering project or Phase Close-Out
  • 5.4 Lessons Learned



With this Three Step process you will have all the tools you need for any Data Engineering project with this in-depth Data Engineering Toolkit.

In using the Toolkit you will be better able to:

  • Diagnose Data Engineering projects, initiatives, organizations, businesses and processes using accepted diagnostic standards and practices
  • Implement evidence-based Best Practice strategies aligned with overall goals
  • Integrate recent advances in Data Engineering and put Process Design strategies into practice according to Best Practice guidelines

Defining, designing, creating, and implementing a process to solve a business challenge or meet a business objective is the most valuable role; In EVERY company, organization and department.

Unless you are talking a one-time, single-use project within a business, there should be a process. Whether that process is managed and implemented by humans, AI, or a combination of the two, it needs to be designed by someone with a complex enough perspective to ask the right questions. Someone capable of asking the right questions and step back and say, 'What are we really trying to accomplish here? And is there a different way to look at it?'

This Toolkit empowers people to do just that - whether their title is entrepreneur, manager, consultant, (Vice-)President, CxO etc... - they are the people who rule the future. They are the person who asks the right questions to make Data Engineering investments work better.

This Data Engineering All-Inclusive Toolkit enables You to be that person.


Includes lifetime updates

Every self assessment comes with Lifetime Updates and Lifetime Free Updated Books. Lifetime Updates is an industry-first feature which allows you to receive verified self assessment updates, ensuring you always have the most accurate information at your fingertips.