![]() ![]() 80% of my interviews, the interviewer asked me how I designed this pipeline and the level of depth i went into (i spun up my own hadoop cluster by cobbling a bunch of ec2 machines together.) I also tried to publish a paper on using novel data to predict crime (also something that interested interviewers). I also did a data fellowship where i built a spark based end to end prod ready data pipeline on aws with working dashboards (also required for one of my big data classes). Not many fresh grads will know about the hadoop ecosystem unless they went out of their way to study it or got a MS that is heavily data focused (thats what I did). You're not going to find many people with data experience and systems design. The skillset for a DE is hard to get for most entry level/new grads. However, most of these interviews treated me more like a senior that included system designs and architecture and not just straight algorithms/ds type questions. I interviewed with 9 companies including FAANGs that hired DEs at the entry level. Most entry level folks do not have the skills for DE. I lead Data engineering teams now as an IC and My path went Marketing Analyst > Database Administrator > Solutions Engineer > BI developer > Data Engineer ![]() A lot of "Entry level" data engineering jobs aka doing Python scripting, ETL managing dataflows etc are labelled "BI Developer" or "Junior Software engineer (data)" or "Junior ETL Engineer" etc. It's a wide and challenging skillset even for someone with a solid grounding in software development. The knowledge required can encompass some or all of relational Databases, Other flavours of Databases, distributed systems like Kafka, Processors like Spark/Flink plus infrastructure and standard application development (sometimes that is also over a distributed system). E.g anything involving kafka even if you're not involved in the actual operational running of the cluster is still encompassing a system that involves replication and partitioning of data with a lot of networking concerns thrown into the mix. Some data engineering roles at the more technical end are closer to distributed engineering roles encompassing infrastructure and distributed applications with multiple integrations. Fuck ups involving permanent data can result in a whole range of fun from dodgy business decisions, to huge amounts of money and time lost to fixing fuckups to data being overwritten and lost forever and/or legal/compliance issues. If a backend has shit logic then you can fix it by deploying better logic. If a companys data is at all valuable to them then it's a big risk to have juniors working on it. In compliance with US Labor Laws pertaining to “Pay Transparency Nondiscrimination Provision”, details can be viewed in this link. In addition, you may also contact the HR Service Center by phone at 97 or email at for additional assistance. ATTENTION APPLICANTS WITH DISABILITIES: If you're unable to access Teradyne's on-line job application due to a disability you may visit any one of our locations including our Corporate Office at 600 Riverpark Drive, North Reading, MA and request a paper application form. Teradyne is an equal opportunity/affirmative action employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, gender, sexual orientation, gender identity and expression, ethnicity or national origin, age, marital status, genetic information, military service, pregnancy, political affiliation, union membership, disability status, protected veteran status, or any other characteristic protected by law. ![]()
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