With a 5 star rating on Course Report, we're one of the top data engineering bootcamps in the world.
"I give this program a 20/10 because it truly changed my life professionally when I wasn't sure if a job in tech could actually happen for me."
"When I tell my software/data engineering friends what we're learning, they're impressed by how much the curriculum covers."
We're committed to helping you secure a job, and we work with you before and after graduation until you get one. Our 2021-2022 graduates' job search results speak for themselves.
Outcomes are for 2021-2022 data engineering bootcamp graduates who were seeking new positions. The graduation rate was 90%. The job placement rate was 100% within 9 months of graduation. Placement and salary numbers include engineering and non-engineering roles, as well as full-time and contract-to-hire positions.
Our curriculum has been built in partnership with industry experts from companies like Facebook, Meltano, and SquareSpace to make sure you learn the top data engineering skills needed to launch your data engineering career.
You’ll work with real datasets right from the start, and learn how to query, organize and transform big data.
We'll start with the fundamentals of Python and SQL, and then go deeper in the key components of backend web development. Along the way, we'll learn design patterns like Model View Controller (MVC) and ETL in Python through the building of Flask APIs.
By the middle of the course, you’ll be able to build an API from scratch, and be well practiced in a professional workflow using bash, git, Github, and testing.
We'll learn to deploy our code with cloud computing tools like AWS and Docker. We'll also build a modern data pipeline in AWS by pulling data from a transactional database into a data warehouse, and then automate our ETL pipelines with Airflow.
Then we'll build an analytics engineering pipeline by using Snowflake for our data warehouse, DBT for data processing, and building dashboards using data visualization tools.
During the second semester, students begin working for outside companies or open source projects. We allocate six hours of in class time per week to perform data engineering projects, with many students performing additional work outside of class.
Past students have worked with companies specializing in healthcare, finance, and public service.
After the program ends we continue to meet twice weekly during post-graduation sessions. This ensures that students continue to improve and review their skills, and have assistance in navigating the interview process. We also regularly connect students with employers to assist with career placement.
While we begin preparing for technical interviews halfway through the program, we ramp this up in post-graduation sessions. Through our technical interview prep, students review fundamentals like data structures and algorithms in Python, data modeling with databases, and practice SQL and Python interview questions.
The best engineering candidates show on-the-job experience. So we built an internship directly into our coding bootcamp.
Halfway through our program, students partner with top companies to deliver part-time work. The corporate placements have spanned a variety of industries, from machine learning, to data engineering, as well as blockchain and cryptocurrency.
Our students and instructors come with a diverse range of talents and backgrounds, and support each other during the program and beyond. From pairing during class to teaming up in the internship, our program is designed to grow your technical and teamwork skills and accelerate your transition to a new job.
"Jigsaw Labs has helped changed the trajectory of my life and career over the course of a few short months. [...] The best part is you'll find yourself with a supportive group, all of whom are looking out for your eventual success."
"Taking this course was one of the best decisions I have made in my life. I have felt so challenged and also so rewarded. The job offers I've received post-course have been awesome. There are not a lot of women of color in this field [but] I really felt super supported and connected to the other folks in my cohort. "
"For someone like me seeking to transition to data engineering from a "non-traditional" background, Jigsaw Labs' curriculum covering in-demand skills, coupled with [the instructor's] attentiveness, has been a huge boost to my domain knowledge and morale."
We designed a high quality, money back guaranteed program that fits around your existing schedule.
By designing our course to be part-time, our students land a new career without quitting their current job.
Our part-time course is designed to fit your schedule. Unlike a full-time course, if you have difficulties with a topic, you can catch up between classes.
Our Zoom-based classes consist of live lectures followed by interactive readings and pairing on labs with instructor assistance.
Book one on one office hours each week for individualized support.
To maintain our 100% placement rate, we provide career coaching and job placement services to all of our graduates.
Post graduation, we continue with bi-weekly classes for technical and career coaching.
Then after you're hired, $833 over 15 monthly payments for a total cost of $13,500
Money back guarantee if do not receive salary of $70,000+ within 9 months of graduating.
Monthly payments only after you're hired
$4,750 paid before each of the two semesters, for a total cost of $9,500
Money back guarantee if do not receive salary of $70,000+ within 9 months of graduating.
Next cohort starts July 24th and runs for 24 weeks.
This is the best way to get a feel for our teaching style, and figure out if you'd enjoy data engineering. Check out our 80 free interactive lessons on topics ranging from Python to Pandas to Machine Learning and Neural Networks.
Learn coding fundamentals by using Python to pull data from the web.
Then create interactive graphs and analyses of your data.
We'll see how Apache Spark's design allows us query large amounts of data quickly. Then we'll use the Pyspark API to query data from AWS.
Learn an essential tool for data analysis and data science with the pandas library.
We'll select, explore and plot data in pandas using flood data from the FEMA API.
We love teaching data engineering. It provides students with a strong foundation in modern fundamentals like Python, SQL, big data and cloud computing. In fact, we often describe the data engineering skillset as backend and cloud engineering skills with a specialization in data pipelines.
Because of a focus on the fundamentals, students have been hired as data engineers, but also backend engineers and data scientists right out of the bootcamp. The reason this skillset is so widely applicable is because collecting, cleaning, and organizing data is critical for drawing insights from this data -- whether for an analytics team, or to feed into a machine leaning model. And this data engineering work is also some of the most challenging work for a data driven organization. Because of this, our students graduate with in-demand data engineering skills, but also skills that allow for a flexible career going forward -- whether students wish to pursue a career in data engineering, machine learning or backend engineering.
Our course from beginning to end is focused on preparing our students to (1) land a software/data engineering position and (2) develop the fundamental skills that allow for a flexible career path going forward.
To achieve the first goal, we designed the course to provide a true onramp to getting hired as a data or software engineer. This starts with admissions, where we only admit students we believe will be successful. And it continues with our curriculum where we only teach material that employers are looking for, and remove the material if the job market changes.
Then, with our internship program in the second half of the course, we select companies who provide projects and mentors that will challenge and help develop the skills of our students. And with our post-graduation classes, we continue meeting twice weekly to ensure our students only improve their skillset. With this combination, 90% of our students have graduated and all of our graduates have landed jobs.
Now to the second point of our focus on fundamentals. We devote 240 hours of classroom time just to backend development skills — primarily Python and SQL. Take a moment to consider how this compares to other online courses or bootcamps. So our philosophy is to not just check off skills on a list but instead to go in depth in a few core skills, and then branch out into other skillsets. This is because for us, programming is a craft. And by keeping Python and SQL as our focus, we develop that craft -- whether through teaching object oriented design patterns, writing clean functions, learning data structures and algorithms, or writing tests. We believe it's this emphasis on the craft of programming that allows our students contribute in their internship halfway through the program, get a job after graduating, and have a flexible career in the future.
To enroll in the course, the first step is to schedule a zoom call to learn more about our online bootcamp. From there, we’ll schedule technical interview that assesses your Python programming skills. The interview will cover the Python material in the first ten lessons of our free Python For Data course.
When we decide whether to admit students into our program, the primary question we ask is, Do we believe this student will get a job upon graduating. We only admit students we are confident will graduate from the course with a positive outcome. In addition, we only admit students who display a curiosity and demeanor that will contribute positively the classroom culture, and internship experience.
We developed our curriculum by (1) talking to hiring industry experts (data engineers, hiring partners, internship partners) and monitoring data engineering communities (2) seeing what is actually asked of our graduates on technical interviews and (3) scraping job postings for data engineering positions to identify required skill sets .
Our goal is for you not just to land your initial job, but to have a flexible career going forward. While we have written curriculum on Apache spark and Kubernetes, we moved it out of our core curriculum as we found it more asked of midlevel engineers, and not our graduates. The same goes for real-time streaming tools like Kafka or big data databases like Hadoop — these skills rarely came up in job interviews. And we do not teach NoSQL as it has not come up in job interviews, and also is only listed in 15% of data engineering job listings.
We have chosen AWS as our cloud provider as it's the industry leader, however students have also learned to use GCP (Google's cloud platform) and Microsoft's Azure platform as part of their internship experience.
One thing to note is that because we constantly update our syllabus, we have a lot of battle tested curriculum outside of our core curriculum (one thousand pages). And all of this is available to our students. So if a student needs to learn a topic for an internship or a job interview, we often have material to ramp them up.
Our internship program runs from weeks 12 to 24 of the course, and provides students with data engineering work experience even before they graduate. In the internship, students work in pairs to complete projects for various tech companies, pro bono. Students work six hours per week during class time, and many students put in anywhere from 2 - 15 hours outside of class, depending on their availability.
To ensure companies are a good match for the program, we speak with various tech companies to make sure they have data engineering projects that students will be able to contribute to, and that the company has mentors to meet with students on at least a weekly basis. Past projects have ranged from building an API for NYC school buses to help manage their bus fleet, to building ETL pipelines for a crypto company, to performing ETL and building a data warehouse for a news analysis organization.
Our data engineering course has a 100% job placement rate among graduates, and there are multiple ways that we achieve this.
1. Ongoing Technical and Career Coaching - Even after our students complete our program, we continue to meet with students twice weekly through ongoing classes to ensure that students continue to improve their skills and progress through the job search. We work with each student individually to help them clean up their Linkedin profile and revise their resume so that they are most attractive to employers.
2. Job placement - We help students in their job search by reaching out to employers on their behalf, gathering information about what employers are looking for and then making the connection.
3. Program structure - Our curriculum focuses on skills that are most in demand by employers. This makes it an easy sell for students to land interviews. In addition, our internship program gives students on-the-job experience that employers love to see in a candidate.
The most popular jobs accepted by our students after graduation were data engineer (over 50%) and backend/software engineer, and data scientist. The median salary of our graduates is $115k, with the lowest salary at $80k per year, and the highest at over $140k.
In addition to data engineer, backend/software engineer, and data scientist, other positions students would be eligible for include business intelligence engineer, analytics engineer, and data analyst.
Data analysts may be asked to use a combination of spreadsheets, SQL, and data visualization tools to draw insights. Similar to a data analyst is business intelligence engineer, which leans more on SQL than tools like excel, and data visualization tools like looker or metabase. Data scientists by contrast generally know either R or Python, as well as related machine learning libraries like SKlearn, and have a strong understanding of statistics to draw insights and perform predictive modeling.
Data engineers by contrast should be well versed in backend engineering skills like Python, and have a deep understanding of SQL, cloud computing, big data tools like snowflake and redshift, and setting up data pipelines with scheduling tools like airflow.
Machine learning engineers typically have both the data engineering skills and the knowledge of machine learning models that a data scientist would have. This is why we recommend getting the data engineering skills first as an entry into a data career.
The primary language is SQL (although not technically a programming language), followed closely by Python. SQL is used query data from big data databases like snowflake or redshift, or standard transactional databases like Postgres. Python is used to pull data from APIs, coerce data, and potentially feed it into a machine learning model. In addition to these programming skills, engineers should be well versed in cloud computing and building data pipelines. Generally, early in their career, data engineers can move between different cloud providers whether Amazon, Microsoft or Google.
Some job descriptions may accept (or prefer) literacy in other object oriented programming languages like Java, as an alternative to Python. Still, Python and SQL are the skillsets requested most.
Students complete their capstone projects through their internship. Student projects have involved automating the deployment of machine learning models, setting up a data pipeline pulling data from slack using an EL tool, performing analysis using DBT, and orchestrating with Gitlab actions. Still other students have worked with cryptocurrency databases to develop dashboards that helped their internship partner understand market fluctuations.
During the post-work class sessions multiple students have worked with Code For Boston, where they are helped to build a data pipeline that tracks police misconduct, or another that tracks lobbying in local government.
Very little. The primarily task of data engineers is to build the data infrastructure needed for machine learning and data analytics. While students may be asked to perform data analysis work as part of their internship, students generally lean on their engineering skills to perform these calculations.