I've been a Senior Data Engineer at Amazon for almost a decade.
But I still remember the first time…
The first time I pulled data from an API,
→ I thought, “Okay cool, this is what data engineering is.”
The first time I set up a basic ETL pipeline,
→ I thought, “Nice, this is just a more organized data pull.”
The first time that pipeline failed at 2 AM,
→ I realized, “Logging and alerts aren’t optional.”
The first time I dealt with duplicate data issues,
→ I learned, “It’s not about moving data, it’s about moving the right data.”
The first time stakeholders questioned my numbers,
→ I realized, “Trust in data starts with quality and documentation.”
The first time I optimized a Redshift query that was running for hours,
→ I thought, “Performance tuning is 50% knowledge, 50% patience.”
The first time I had to explain partitioning, file formats, and data latency to a non-tech team,
→ I realized, “You don’t just build pipelines, you bridge gaps.”
The first time I worked on GDPR/PII compliance,
→ I learned, “Data engineering is as much about governance as it is about pipelines.”
The first time I led the migration to a new data platform,
→ I saw, “Tooling keeps changing, but the fundamentals never do.”
The first time I mentored a junior engineer,
→ I realized, “The job isn’t just about writing clean code, it’s about building confident people.”
And the first time I stepped back to look at everything we built,
→ I knew, “You don’t become a great data engineer by knowing everything. You grow by staying curious and accountable, every single day.”
Repost if this resonates.
P.S. Follow me for insights around data engineering, and resources on how to crack a role in Big Data .
But I still remember the first time…
The first time I pulled data from an API,
→ I thought, “Okay cool, this is what data engineering is.”
The first time I set up a basic ETL pipeline,
→ I thought, “Nice, this is just a more organized data pull.”
The first time that pipeline failed at 2 AM,
→ I realized, “Logging and alerts aren’t optional.”
The first time I dealt with duplicate data issues,
→ I learned, “It’s not about moving data, it’s about moving the right data.”
The first time stakeholders questioned my numbers,
→ I realized, “Trust in data starts with quality and documentation.”
The first time I optimized a Redshift query that was running for hours,
→ I thought, “Performance tuning is 50% knowledge, 50% patience.”
The first time I had to explain partitioning, file formats, and data latency to a non-tech team,
→ I realized, “You don’t just build pipelines, you bridge gaps.”
The first time I worked on GDPR/PII compliance,
→ I learned, “Data engineering is as much about governance as it is about pipelines.”
The first time I led the migration to a new data platform,
→ I saw, “Tooling keeps changing, but the fundamentals never do.”
The first time I mentored a junior engineer,
→ I realized, “The job isn’t just about writing clean code, it’s about building confident people.”
And the first time I stepped back to look at everything we built,
→ I knew, “You don’t become a great data engineer by knowing everything. You grow by staying curious and accountable, every single day.”
Repost if this resonates.
P.S. Follow me for insights around data engineering, and resources on how to crack a role in Big Data .
