AWS Data Engineer Tutor Needed

@tangramua

Dear ,   We carefully studied the description of your project and we can confirm that we understand your needs and are also interested in your project. Our team has the necessary resources to start your project as soon as possible and complete it in a very short time.   We are 25 years in this business and our technical specialists have strong experience in Python, Ruby on Rails, SQL, MySQL, Amazon Web Services, AWS Lambda, ETL, PySpark and other technologies relevant to your project.   Please, review our profile https://www.freelancer.com/u/tangramua where you can find detailed information about our company, our portfolio, and the client's recent reviews.   Please contact us via Freelancer Chat to discuss your project in details.    Best regards, Sales department Tangram Canada Inc.     

@ahmadayaz

With over five years of hands-on experience in AWS services, including S3, Glue, Redshift, and more, I can guide you in establishing a strong foundation in AWS Data Engineering. Tailoring sessions to your pace and skill level, I offer personalized mentoring to help you grasp fundamental concepts and work on practical applications. Our structured curriculum will cover ETL/ELT pipeline design, data lake versus data warehouse, and integrating Python/PySpark with SQL for analytics. Through real-world scenarios, you will gain the skills to handle projects confidently. Weekly sessions will focus on hands-on learning, culminating in a portfolio project to showcase your expertise. Let's begin with a trial session to kickstart your journey towards becoming a proficient AWS Data Engineer.

@C0RETECHIES

Hi there, I understand you need a mentor to master the AWS data engineering workflow. This means going beyond individual tools and learning how to build end-to-end pipelines: ingesting data into an S3 data lake, transforming it with Glue and Lambda, structuring it in Redshift for warehousing, and running analytics with Athena. I can guide you through building these systems from scratch. Technical approach: We'll follow a practical, hands-on curriculum: - Foundations: S3 data lake design & IAM security. - Batch ETL: Building pipelines with AWS Glue (PySpark) and serverless Lambda. - Analytics: Data warehousing with Redshift and ad-hoc querying with Athena. - Streaming & Orchestration: Intro to Kinesis and managing workflows with Step Functions. Core modules: The focus will be on practical skills: writing transformation code (Python/SQL), data modeling, pipeline automation, and cost optimization. We will build a complete, production-style pipeline for your portfolio. Relevant systems: My experience comes from building data-heavy AWS systems. - Bison360 ERP: Utilizes AWS S3 and PostgreSQL for complex inventory and transaction data pipelines. - TDTY Platform: Leverages AWS RDS and S3 to manage and serve large volumes of structured content. Implementation strategy: Our sessions will be 80% hands-on coding and console work, 20% theory. We'll start with a simple pipeline and add complexity each week, ensuring you're building a real-world project that demonstrates your skills. Regards, Rohit

@corpmember29

Hello, I am a honest dedicated full time AWS Data Engineer developer. I read your requirements carefully and understood very well about the project scope and start working accordingly in stages. I have 10+ years of experience in technology, with practical experience in data engineering, SQL, Python, cloud-based data pipelines, and AWS services. I can guide you from fundamentals to real-world implementation rather than focusing only on theory. The learning path can cover S3, Glue, Athena, Redshift, Lambda, Kinesis, EMR, Step Functions, RDS/DynamoDB, ETL/ELT, data lakes, data warehouses, Python, PySpark, and SQL, followed by a complete portfolio project and optional AWS Data Engineer Associate certification preparation. successfully implement this project from start-to-finish. Let's come together and create a platform that not only propels your business but also stands out prominently within the marketplace. Thanks >>>>>>> We'll share our portfolio in Chat. Let's talk further speak over the freelancer call or chat. <<<<<<

@denisSolution

Hi, This makes sense—a mentor who’s actually built pipelines is worth more than another certification guide. I’ve worked on a few AWS-heavy projects where the main lesson was how to keep things debuggable under real data volume. For example, on a data-heavy SaaS I helped build, we used Glue for ETL, Redshift for analytics, and Step Functions to orchestrate the flow. The tricky part was tuning partitions and file formats in S3 so Athena queries stayed predictable. We can start by mapping your current level and then work through a simple ETL example—say, taking raw logs, cleaning them with PySpark, and landing the results in Redshift. Once the pattern clicks, we can layer in monitoring, cost control, and failure handling. The biggest unknown is usually how much raw data you’ll be working with in practice; we can adjust the tooling as we hit that wall. If you’re interested, the first session can be a quick skills check and a lightweight roadmap. Thanks, Denis

@DeveloperStation

Hi, Most of my AWS work is production data and DevOps, not tutoring, but that hands-on side is exactly what makes the teaching useful. I built AWS integration and migration pipelines on client contracts, including a website integration workflow and an AWS migration. DevOps AWS work: Adil Upwork, 5 stars, "Great to work with and will work with them again." One question shapes the curriculum: do you want to move toward the Data Engineer Associate cert, or focus purely on real pipeline building for your portfolio? That changes whether we spend early sessions on Glue and Redshift labs or on exam-style Athena and Kinesis coverage. I'd suggest a trial session first: I map a personal learning path, then we run one hands-on S3 to Glue to Athena lab so you can judge the fit before committing weekly. Which cadence suits you, weekly or biweekly? Adil

@mayr81

I can mentor your AWS Data Engineering journey with a structured, hands-on curriculum that starts from fundamentals and builds toward production-ready pipelines. We'll cover S3, Glue, Redshift, Athena, Kinesis, Lambda, EMR, Step Functions, and RDS/DynamoDB, connecting each service to the data lifecycle (ingest → transform → store → query → serve). My approach: - Core mental models: data lake vs. data warehouse, schema/partitioning, cost-aware design - ETL/ELT pipeline design: orchestration patterns, retries, idempotency, and monitoring - Practical labs: hands-on builds using Python/PySpark and SQL, with incremental portfolio-style project outputs - Real-world guidance: designing pipelines you can explain confidently in interviews and on a resume - Optional cert prep: targeted walkthroughs aligned to the AWS Data Engineer - Associate objectives You’ll leave each 1:1 session with concrete artifacts (not just explanations) and a clear next step on the learning path.

@pasion0128

★•══•★ Hi client ★•══•★ I’ve worked hands-on with AWS data engineering tools like S3, Glue, Redshift, Athena, Kinesis, Lambda, and more in real projects, so I know the ins and outs beyond just certifications. I focus on breaking down complex concepts into simple steps and love sharing practical examples to make learning stick. My approach? We’ll start with core fundamentals, dive into hands-on labs with Python and PySpark, build ETL pipelines together, and discuss real-world scenarios you can showcase in your portfolio. I’m all about making sessions interactive and tailored to your pace—no boring lectures here. I’m happy to help prep for the AWS Data Engineer Associate exam too if you want. How about we kick off with a trial session to see what works best for you? Looking forward to hearing how you want to shape your learning journey! Best regards, Rico

@durgesh8527

As an accomplished AWS Data Engineer with a history of successful projects, I am well-equipped to guide you through your learning journey. My extensive experience in AWS services such as Lambda, S3, Glue, Redshift, Athena, Kinesis, ensures that you receive in-depth understanding of each core concept. But it doesn't stop there; I will show you how to integrate these services cohesively into real-world use cases and share examples of my past projects that demonstrate practical applications. Teaching is in my DNA – being able to break down complex concepts and explain them in a concise and understandable way is what my clients value most about my tutoring style. In line with your stated need for a practical curriculum, my lessons will be bolstered by hands-on labs and sample projects aimed at equipping you with the skills demanded on the job market. Additionally, I can help you construct a portfolio chock-full of impressive projects that enhance your resume's appeal.

@sufyanasmat38

Hi, I have hands-on experience with AWS data engineering, Python, SQL, ETL/ELT pipelines, and cloud-based data workflows. I can guide you through AWS services such as S3, Glue, Redshift, Athena, Lambda, Kinesis, EMR, and Step Functions using practical examples rather than just theory. I’ll create a structured learning path starting with data lakes, warehouses, pipeline architecture, and SQL, then move into PySpark, AWS services, and real-world projects. Each session can include hands-on labs and practical exercises so you build skills you can confidently use in your portfolio. I’m comfortable working with beginners and explaining complex concepts step-by-step. We can also include AWS Data Engineer Associate certification preparation once the fundamentals are strong, with ongoing weekly or biweekly sessions based on your pace.

@demivision

As an experienced software developer with over 20 years in the industry, I have evolved along with technology and data engineering has become one of my core areas of expertise. I have hands-on experience working on AWS data engineering projects that have involved the core services you mentioned such as S3, Glue, Redshift, and Athena to name a few. I'm well-versed in ETL/ELT pipeline design and can effectively guide you through the complexities of data lakes vs. data warehouses. When it comes to tutoring, my experience goes beyond the technicalities — it's about effective communication and understanding your unique needs as a learner. I pride myself on my ability to explain complex concepts in a simple manner so that anyone new to a field can grasp it easily. I don't just stop at theory, I provide practical examples and real use cases for better comprehension. Lastly, let's not forget that my work is incomplete until it enables you to showcase your skills proficiently. As we progress through the curriculum, we will also be working on real-world projects that you can proudly add to your portfolio or resume. My aim is not just to help you pass certification exams but rather build strong foundational skills that will set you up for long term success in the field of AWS data engineering. Let's embark on this journey together!

@dataspro

Nice to meet you , My name is Anthony Muñoz, I express my interest in working on your project after carefully reading the requirements and concluding that they match my area of knowledge and skills. I am currently the lead engineer for the IT agency DSPro and I have more than 10 years of experience in the field. I have successfully completed a large number of similar jobs and I consider your project to be a challenge in which I would like to work and be able to make it a reality. Please feel free to contact me, it will be my pleasure to help you. I greatly appreciate the time provided and I remain attentive to any questions or concerns. Greetings

@Noman452

Hi, I can provide practical 1:1 AWS Data Engineering mentoring focused on real-world skills and hands-on projects. I’ll cover S3, Glue, Redshift, Athena, Lambda, Kinesis, EMR, Step Functions, Python/PySpark, SQL, ETL/ELT, and data lake/warehouse architecture. I’ll tailor the learning path to your level, build portfolio-ready projects with you, and can also help prepare you for the AWS Data Engineer certification.

@richard031596

You need structured 1:1 mentoring that builds practical AWS data-engineering ability, not just certification knowledge. I can guide you through S3-based data lakes, Glue catalogues and ETL jobs, Athena, Redshift, Lambda, Kinesis, EMR, Step Functions, RDS/DynamoDB, IAM, monitoring and cost control. Sessions will combine concise explanations, live labs, architecture decisions, debugging and assignments reviewed together. Suggested path: Python/SQL foundations; S3/IAM; batch ETL with Glue and PySpark; Athena and Redshift analytics; orchestration with Step Functions; streaming with Kinesis/Lambda; EMR; security, testing and optimisation; then an end-to-end portfolio project with documentation and interview-ready architecture discussion. Certification objectives can be mapped alongside the practical work. I’m available for recurring weekly sessions and recommend beginning with one trial lesson and a skills assessment. What is your current Python/SQL level, and are you prioritising employment, certification, or both?

@carlosd178

Hello, As a result of reviewing your requirements, I understand you’re looking for ongoing 1:1 AWS Data Engineering mentoring focused on practical skills, not just theory or certification prep. I have experience with AWS data pipelines, Python, SQL, ETL/ELT, S3, Glue, Redshift, Athena, Lambda, Kinesis, EMR, Step Functions, RDS/DynamoDB, and PySpark, and I’m comfortable explaining both architecture and hands-on implementation. My suggested learning path would start with AWS fundamentals, S3-based data lakes, IAM, SQL/Python, and ETL concepts, then move into Glue/Athena/Redshift, Lambda and Step Functions orchestration, Kinesis streaming, EMR/PySpark, monitoring, optimization, and finally 2–3 portfolio-ready projects that simulate real production pipelines. For each session, I’d combine concept explanation with a practical lab, code review, and a small assignment so the knowledge compounds week by week. I can also include AWS Data Engineer Associate preparation once the fundamentals are solid. I’m open to weekly or biweekly sessions and a trial session first. One quick question: what is your current level with Python, SQL, and AWS? Best regards, Carlos

@KasjanovaEste

Hi, i’m Fernando, a senior full‑stack developer with extensive AWS data‑engineering experience and a passion for teaching. I understand you want to move from zero to a portfolio‑ready AWS data engineer, mastering services like S3, Glue, Redshift, and building real ETL pipelines. A practical insight: most beginners struggle with the transition from a data lake to a data warehouse, so we’ll focus on that bridge early. My approach mixes short theory bursts with guided labs, using Python and PySpark on Glue jobs while we iterate on a mini project that mimics a production pipeline. This keeps sessions focused, reduces the risk of idle learning, and leaves you with reusable code for future jobs. Recently i mentored a junior analyst who needed to showcase an end‑to‑end pipeline for a resume. i built a sample that extracted CSVs from S3, transformed them with Glue‑PySpark, and loaded into Redshift, then helped them present the results in Athena. The learner completed the AWS Data Engineer Associate exam and added the project to his portfolio. Questions: 1. Which specific business domain or dataset are you most interested in using for the hands‑on labs? Knowing this will let me tailor examples that resonate with your career goals. 2. Do you prefer a certification‑focused curriculum or a broader engineering skill set first? Your answer will shape the pacing and depth of each session. I look forward to shaping a learning path that fits your schedule and goals. Thanks

@alluankit01

Hi, I can mentor you 1:1 in AWS Data Engineering with a practical, hands-on learning path covering core services, pipeline design, Python/PySpark, SQL, and portfolio-ready projects. My approach will be to start from fundamentals, then move into real labs using S3, Glue, Athena, Redshift, Lambda, Kinesis, EMR, Step Functions, RDS/DynamoDB, and basic CI/CD or deployment patterns where useful. Suggested learning path: * AWS data engineering basics * Data lake vs warehouse concepts * S3 data lake setup * Glue crawlers and ETL jobs * Athena SQL analysis * Redshift warehouse basics * Lambda automation * Kinesis streaming basics * EMR/Spark/PySpark labs * Step Functions orchestration * End-to-end ETL project * Resume/portfolio project guidance * AWS Data Engineer Associate prep support I’m comfortable with AWS, Python, PySpark, SQL, ETL/ELT pipelines, data lakes, analytics workflows, and explaining complex concepts in a simple practical way. I’ll focus on hands-on sessions, small homework labs, code reviews, and real-world examples so you can build confidence and create projects that are useful for your resume. Best regards Ankit

@thiagofoz87

Your goal requires more than certification-focused explanations — the fastest way to build real AWS Data Engineering skills is through practical architecture decisions, hands-on labs, and iterative project work that mirrors production environments. I can help structure a learning path that starts from the fundamentals and progresses toward building complete AWS-native data pipelines. The sessions can combine theory, architecture discussions, guided coding, and real implementation exercises. A practical curriculum could include: - Data lake vs. warehouse architecture and ingestion patterns - S3, Glue, Athena, Redshift, Lambda, Step Functions, Kinesis, and EMR usage in real workflows - ETL/ELT pipeline design and orchestration - SQL analytics and data modeling fundamentals - Python and PySpark for transformations and automation - Event-driven processing and scalable cloud patterns - Portfolio-grade projects using realistic datasets and deployment flows - AWS certification preparation if desired My background is focused on building scalable cloud-native systems with AWS, distributed services, asynchronous processing, APIs, and production-grade backend architectures. I also work regularly with Python, SQL, Postgres, MongoDB, messaging systems, and automation workflows, which aligns well with modern AWS data engineering practices. For the mentoring format, I recommend recurring weekly sessions with hands-on exercises between meetings, plus shared examples and architecture reviews to accelerate learning consistency.

@dinobartolome

Your learning path makes sense. Starting from S3 and Lambda fundamentals, building up to Glue and Redshift, then tying it together with real ETL pipelines you can put on a resume. That progression is how production data engineers actually learn it. I've got 10+ years in tech with production AWS work across S3, Lambda, RDS and DynamoDB. Python and SQL are daily drivers for me. I've built data processing workflows and worked with Docker-based deployment pipelines on AWS, so covering the foundational services, Python scripting for data work and SQL for analytics is solid ground. Honest gap though: Glue, Redshift, Athena, EMR and PySpark aren't part of my daily work. I can teach the AWS fundamentals and Python/SQL side confidently, but if you specifically need deep hands-on guidance through Glue jobs and Spark clusters from someone who runs those in production, you'd want a dedicated data engineer for those modules. If that split works for you, I can cover the foundation and we build from there. Want to do a trial session this week so you can see if the teaching style clicks?