DAVID JOHNSON
Data Engineering Manager
linkedin.com/in/david-johnson-data-engineering
github.com/djohnsondev
djohnson-tech.dev
Skills
Python, Java, Scala, Spark SQL, AWS Redshift, Apache Kafka, Talend, Docker
Certifications
AWS Certified Big Data Specialty
Certification that validates expertise in designing, building, and securing data lakes and big data frameworks on AWS.
Microsoft Certified: Azure Data Scientist Associate
Certification that demonstrates the ability to design and implement data solutions using Microsoft Azure.
Professional Summary
Data Engineering Manager with over 5 years of experience in architecting data infrastructure for high-transaction financial services platforms. Successfully led a migration to Apache Hadoop and Spark, enhancing data processing capabilities by 40% and reducing costs by 25%. Skilled in managing cloud-based systems like AWS Redshift and Google BigQuery.
Work Experience
Senior Data Engineering Manager
01/2022
Tech Company Inc
San Francisco, CA
•
Led a team of 5 engineers to deliver microservices architecture, reducing deployment time by 60%
•
Built automated testing pipeline, catching 95% of bugs before production deployment
•
Mentored 3 junior developers, enhancing team performance and skills
•
Optimized database queries, reducing API response time from 500ms to 120ms
Data Engineering Manager
06/2020 - 12/2021
Previous Company Inc.
San Francisco, CA
•
Architected and deployed a data lake on AWS S3, reducing storage costs by 20%
•
Developed a data pipeline using Apache Kafka and Spark, improving real-time processing speed by 50%
Data Engineer
12/2018 - 05/2020
Another Company LLC
San Francisco, CA
•
Created a data warehouse using Snowflake, handling 2M daily transactions without downtime
•
Designed and implemented a scalable ETL process, reducing data processing time by 80%
Education
Master's Degree in Computer Science
09/2017 - 05/2020
University of California, Berkeley
Berkeley, CA
Projects
Data Lake Visualization Dashboard
Developed an open-source data lake visualization dashboard using Apache Superset and AWS services to provide real-time insights into large-scale datasets.
github.com/djohnsondev/data-lake-visualization-dashboard
ETL Pipeline for Open Data Initiative
Created an efficient ETL pipeline using Airflow and Python to process open-source datasets, enhancing data accessibility for researchers.
Join thousands who transformed their careers with AI-powered resumes that pass ATS and impress hiring managers.
Loading template...
Loading template...
This resume format is highly effective for ATS due to its clear structure and inclusion of relevant keywords such as 'Data Engineering Manager,' 'architecture,' and 'scalability.' The use of bullet points highlights key achievements and responsibilities, making it easier for automated systems to parse the information quickly. Additionally, including a mix of technical skills and soft skills provides a comprehensive view of the candidate's capabilities.
Want to know how your Data Engineering Manager resume performs? Use our free ATS Resume Score tool to get instant feedback on your resume's ATS compatibility for Data Engineering Manager positions. Upload your resume below and receive detailed analysis with actionable recommendations to improve your chances of landing interviews.
Instant ATS-friendly analysis with recruiter-ready suggestions to land 2x more interviews. No signup required for basic score.
Import your profile to unlock automated fixes, personalized career tips, and smart job matching.
or click to browse files
Supports PDF and DOCX • Max 20MB
Expert guidelines and best practices for each section of your resume.
First Name Last Name City, State, Zip Code Phone Number | Email Address LinkedIn Profile URL | Portfolio URL (Optional)
Your contact information is the first section recruiters see. Keep it concise and professional. Ensure your email address is appropriate (e.g., [email protected]). Include your LinkedIn profile for a comprehensive view of your professional journey. A portfolio or personal website is recommended for creative, technical, or design roles.
Do not include your full physical address (street number/name) for privacy reasons. Avoid including personal details like marital status, age, photo, or social security number unless specifically required in your country. Don't use unprofessional email addresses.
See clear examples of how to format contact details effectively.
John Doe 1234 Random St, Apt 56 New York, NY 10001 [email protected] github.com/aliciacode Single, 28 years old
John Doe New York, NY (555) 123-4567 | [email protected] linkedin.com/in/johndoe | github.com/johndoe | johndoe.dev
Professional Title Result-oriented [Role Name] with [Number] years of experience in [Key Skills/Industries]. Proven track record of [Major Achievement]. Skilled in [Key Technologies/Skills]. Committed to delivering [Specific Value] for [Target Industry/Company type].
A professional summary is your elevator pitch. It should be 3-5 sentences long, summarizing your experience, key skills, and major achievements. Tailor it to the job description by using relevant keywords. Focus on what makes you unique and the value you bring to potential employers.
Avoid generic objectives like 'Looking for a challenging role to grow my skills.' Recruiters want to know what value you bring to them, not what you want from them. Don't use first-person pronouns (I, me, my). Keep it concise and impactful.
Compare a weak objective with a strong professional summary.
Objective: I am a hard-working individual looking for a Data Engineering Manager position where I can learn new things and advance my career.
Senior Data Engineering Manager with 6+ years of experience in architecting scalable data infrastructures. Successfully transitioned from small-scale to enterprise-grade solutions, reducing data processing costs by 25%. Expert in cloud platforms (AWS, Azure), big data frameworks (Apache Spark, Kafka), and ETL tools (Talend). Passionate about fostering a culture of innovation and mentoring junior engineers.
Technical Skills - Languages: [List] - Frameworks: [List] - Tools: [List] Soft Skills - [Skill 1], [Skill 2], [Skill 3]
Group your skills logically (e.g., Languages, Frameworks, Tools). Focus on hard skills relevant to the job. List skills in order of proficiency or relevance. Soft skills are better demonstrated through bullet points in your experience section rather than a bare list.
Do not list skills you are not comfortable using in an interview. Avoid using progress bars or percentages to rate your skills (e.g., 'Java: 80%') as they are subjective and often misinterpreted. Don't include outdated technologies unless specifically required.
Practical example showing do's and don'ts for skills
Java: Advanced, Python: Intermediate, Spark SQL: Basic
Leadership, problem-solving, data analytics (not relevant to the role)
Job Title | Company Name | Location Month Year – Month Year - Action Verb + Context + Result (Quantified) - Led [Project] resulting in [Outcome]... - Collaborated with [Team] to implement [Feature]...
This is the core of your resume. Use reverse-chronological order (most recent first). Start each bullet with a strong action verb. Focus on achievements and impact, not just duties. Use numbers to quantify your impact (dollars, percentages, time saved, users affected). Show progression and increasing responsibility.
Avoid passive language like "Responsible for..." or "Tasked with...." Don't list every single daily task; focus on significant contributions and measurable outcomes. Avoid jargon that recruiters outside your field won't understand.
Practical example showing do's and don'ts for experiences
Responsible for developing data pipelines using Apache Spark and Hadoop.
Developed data pipelines using Apache Spark and Hadoop, increasing processing speed by 30%.
Led a team of engineers in building the company's data engineering infrastructure.
Led a team of five engineers to build an enterprise-scale data engineering infrastructure on AWS Redshift, reducing storage costs by 25%.
Degree Name | University Name | Location Month Year – Month Year - Relevant Coursework: [Course 1], [Course 2] - Honors/Awards: [Award Name] - GPA: X.X (if above 3.5)
List your highest degree first. If you have significant work experience, keep the education section brief. Include your GPA only if it is above 3.5 or if you are a recent graduate. Highlight relevant coursework, academic projects, honors, or leadership roles.
Do not include high school details if you have a college degree. Avoid listing every single course you took; select only the most relevant ones. Don't include graduation dates from decades ago if age discrimination is a concern in your field.
Practical example showing do's and don'ts for educations
Bachelor of Science in Computer Engineering | University of California, San Diego | San Diego, CA September 2014 – May 2018 - Courses: Introduction to Computer Science, Data Structures and Algorithms, Operating Systems. - GPA: 3.6
Master’s Degree in Data Science | University of California, Berkeley | Berkeley, CA September 2017 – May 2020 - Relevant Coursework: Big Data Technologies, Cloud Computing, Advanced Algorithms. - Honors/Awards: Dean's Honor List
Project Name | Technologies Used - Briefly describe what you built and its purpose - Highlight a specific technical challenge you solved - Link to GitHub or live demo if available
Projects are excellent for demonstrating practical skills, especially if you lack work experience or are changing careers. Include a link to the GitHub repo or live demo if possible. Focus on projects that show problem-solving skills and relevant technologies for the target role.
Don't include trivial tutorials unless you significantly expanded on them. Avoid projects that are outdated, incomplete, or irrelevant to the role you're applying for. Don't just list technologies—explain what you built and why it matters.
Practical example showing do's and don'ts for projects
Created a basic CRUD app using Python Flask. Added some simple HTML pages.
Developed a scalable web application using Python Flask to manage user data in real-time, enhancing the system’s performance by 50% through asynchronous processing.
Installed Apache Kafka and Spark locally following tutorial instructions without any additional customization or improvements.
Implemented an advanced ETL pipeline for a financial services company using Apache Kafka and Spark, streamlining data ingestion and processing to meet real-time compliance requirements.
Common questions about this role and how to best present it on your resume.
Strong technical expertise in data modeling, ETL processes, and cloud platforms like AWS or Azure.
Highlight relevant work experience, certifications, and projects that demonstrate your skills and knowledge.
Leading the data engineering team, designing scalable architectures, and ensuring data quality and security.
Critical, as most modern data pipelines are built on cloud platforms for scalability and cost-efficiency.
Join thousands who transformed their careers with AI-powered resumes that pass ATS and impress hiring managers.
Candidates who tailor their resumes to the job description get 2.5x more interviews. Use our AI to auto-tailor your CV for every single application instantly.