Utility Data Engineer
A Data Engineer contract prep course. Every module maps to a line of the target role's skill list — we teach what's missing and refresh what you already have, so you can speak to every requirement.
Study mode
Two modes for two situations. Switching this toggle re-ranks every module and lesson list.
Crash course · ~30 hrs total · aimed at interview-ready across every required skill while the role is active. Skips depth, hits every topic, gets you speaking with specificity.
Deep dive · ~80+ hrs total · aimed at on-the-job hardening once you're in the role. Extra lessons, optional labs, and the full capstone.
Your progress
The target role — required skills
Paraphrased from the role description. Every line below is a required skill area; every module maps to one or more.
Required skills
Your background ↔ role skills
Visual coverage map. Every skill area is either Strong, Partial, or Gap based on the background you came in with.
Git, Jenkins, CI/CD Strong
Deep CI/CD background: pipeline authoring, GitOps, container-image maintenance, runner infrastructure, policy-driven release flows at regulated-industry scale.
→ 20–30 min DE-specific refresher only. Go
Python fundamentals Strong
Years of Python scripting for ops automation and custom tooling. Comfortable with packaging, logging, testing — standard senior-dev fluency.
→ Skip Python 101. Focus on DE-specific libs (pandas, boto3, snowflake-connector). Go
SQL fundamentals Strong
SQL experience across multiple engines. Transactional and analytical query patterns both familiar.
→ Skip SQL basics. Snowflake-specific dialect + advanced patterns. Go
AWS generalist Strong
Hands-on with AWS core services — storage, IAM, RDS, cost tuning. Container/compute platform exposure.
→ Skip "what is AWS." Focus on data services (Glue/Lambda/Step Functions). Go
AWS data services Partial
AWS core is familiar, but the data-specific services (Glue, Step Functions, Lambda-for-data) are less rehearsed.
→ Deep module: all 4 services with hands-on free-tier labs. Go
ETL/ELT concepts Partial
Earlier in your career you built a scheduled data-ingest pipeline into a columnar warehouse. Same pattern; the tools have moved on.
→ Re-frame as ELT for modern cloud warehouses; map old concepts to new tools. Go
Large-scale optimization Partial
Strong infrastructure-optimization instincts (database IOPS tuning, resource rightsizing) but not Snowflake-specific.
→ Snowflake Query Profile + warehouse sizing in the capstone. Go
Snowflake Gap
No direct Snowflake experience. Biggest investment in this course.
→ Full module + free-tier hands-on labs. Go
Matillion (ELT tool) Gap
No direct exposure. Similar platforms (dbt, Airflow) also not in the background.
→ Full module, Matillion Hub trial account. Go
Snowflake governance (RBAC) Gap
General security-ops experience exists, but not Snowflake RBAC / column-level security specifically.
→ Full module with PII-handling utility-domain scenarios. Go
Kafka / Kinesis Gap
Log-adjacent experience (search/logging stacks) — not the same as Kafka/Kinesis for data pipelines.
→ Full module with local Docker Kafka + Kinesis free-tier. Go
Utility industry data Gap
Prior domains don't include utilities. The vocabulary and data systems are new.
→ Capstone module: CIS/GIS/AMI/OMS crash course + meter-to-cash pipeline. Go
Graduate degree Partial
Undergraduate CS + substantial hands-on experience. The role description notes "or foreign equivalent" — experience-based equivalence is common for contracts.
→ Not a curriculum gap. Lean on experience + domain fluency to offset.
Coverage summary
You arrive with ~50% of the required foundations covered (Git/Jenkins, Python, SQL, AWS generalist). This course targets the remaining half with depth on Snowflake, Matillion, streaming, governance, and the utility domain — the pieces that actually differentiate you for this role.
What will this cost you?
The course itself is free — no accounts, no subscriptions. Hands-on labs use free tiers of real services. Here's the honest breakdown, with cost guardrails for each.
❄️ Snowflake
Free: 30-day trial, $400 in credits, any edition. Sign up with a work or personal email. No credit card required.
Cost trap: Forgetting to suspend your warehouse. A small warehouse left running 24/7 for a week ≈ $35.
Guardrail: Create all warehouses with AUTO_SUSPEND = 60. Walked through in Exercise 2.1.
☁️ AWS
Free tier: S3 5GB forever, Lambda 1M invocations/mo forever, Step Functions 4k transitions/mo forever. Glue gets no free tier — ~$0.44/DPU-hour.
Cost trap: Accidentally running Glue on a huge crawler schedule. Kinesis shards ($0.015/hr each) if you spin up and forget.
Guardrail: Billing alarms at $1 and $5. Set them in Exercise 4.1. If you have an AWS credit source available, use free tier first anyway — easier habit.
🧱 Matillion
Free: Matillion Hub offers a 14-day trial with full Data Productivity Cloud access. After trial, free-tier Data Productivity Cloud is metered in "credits" (small jobs = small credits).
Cost trap: Running transformations against a large Snowflake warehouse — the compute bill is on Snowflake's side.
Guardrail: Use X-SMALL Snowflake warehouse only during Matillion lessons. Trial gives you enough runway for Module 5.
📡 Kafka
Free: Local Docker compose. Zero cost, runs on your laptop.
Alternative: Confluent Cloud free tier ($400 credits, no CC for trial). Only needed for Snowpipe Streaming exercise.
Guardrail: We'll default to local Docker in the lessons. Confluent is optional deep-dive.
🪣 Kinesis
Free tier: None for Kinesis Data Streams. ~$0.015/shard/hr + $0.014/M records.
Cost trap: Forgetting a shard for a week = ~$2.50. Not catastrophic but it adds up.
Guardrail: The lesson walks you through create → produce 100 records → delete in under 10 minutes. Total cost < $0.05.
🐙 Jenkins
Free: Local Docker container. Zero cost.
Alternative: You may already have work/personal Jenkins access.
Guardrail: None needed.
Total out-of-pocket for the whole course if you stick to the guardrails: realistically $0–$3. The Snowflake $400 of credits is more than enough even for Deep Dive mode.
Modules
Click into any module for the full skill-mapping, lesson list, and hands-on labs. The crash tag means a lesson is on the crash-course path; deep is added in deep-dive mode.