Big Data Engineer → Senior Data Engineer
A realistic transition from Big Data Engineer to Senior Data Engineer: transferable skills, the gaps to close, a step-by-step learning plan and alternative routes. Estimated skill overlap 74%. Difficulty: Easy.
Difficulty: Easy6 stepsDuration: 4 to 8 monthsOverlap: 74%
Transition from Big Data Engineer to Senior Data Engineer.
Transferable skills
- Data platform architecture
- Python / Scala
- Spark / distributed processing
- Cloud data platforms
- Streaming (Kafka)
- Data modelling
Skills to learn
- Orchestration (Airflow)
- Mentoring
Step-by-step learning plan
1. Build on your Big Data Engineer experience
2–4 weeks
Leverage
- Data platform architecture
- Python / Scala
- Spark / distributed processing
- Cloud data platforms
Map transferable skills — Data platform architecture, Python / Scala, Spark / distributed processing — against what a Senior Data Engineer needs.
2. Learn the core of the Senior Data Engineer role
6–10 weeks
Learn
Technologies
dbtBigQueryRedshift
Cover the biggest gaps first: Orchestration (Airflow).
3. Master advanced Senior Data Engineer skills
6–10 weeks
Learn
Technologies
AWSGCPPrefect
Handle the advanced topics: Mentoring.
4. Get productive with the Senior Data Engineer toolchain
4–8 weeks
Technologies
dbtBigQueryRedshiftAWSGCPPrefect DagsterLinux
Be hands-on with dbt, BigQuery, Redshift and the rest of the target stack.
5. Practise Senior Data Engineer work in real conditions
4–8 weeks
Ship a portfolio project or take on Senior Data Engineer tasks at work.
6. Get certified and position yourself as a Senior Data Engineer
4–8 weeks
Technologies
Google Professional Data EngineerAWS Data Analytics SpecialtyDatabricks Certified Data Engineer
Update your CV and LinkedIn around Senior Data Engineer, then apply to target roles.