Data Engineer - Onsite - W2

Below is a ready‑to‑send application package that you can copy‑paste into your email or applicant‑tracking‑system (ATS).
It includes:

1. Targeted cover‑letter (concise, 4 paragraphs, 350 words) – highlights the exact skills the posting asks for and shows why you’re a perfect fit for the Dallas/Richmond/Chicago/​McLean locations.
2. Resume “Data Engineer” section – bullet‑points written in the ATS‑friendly “action‑verb + technology + impact” format, with quantifiable results.
3. Quick interview cheat‑sheet – the top 8 things the hiring manager will likely probe, plus concise talking‑points and a one‑sentence “STAR” story you can adapt on the spot.

Feel free to edit the placeholders (e.g., [Your Name], [Current Company], [X] years) with your own details.

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1️⃣ Cover Letter (PDF‑ready)

[Your Name]
[Street Address] • Dallas, TX 75201
[Phone] • [Email] • LinkedIn: linkedin.com/in/[your‑handle]

April 5 2026

Hiring Committee – Data Engineering
[Company Name]
[Company Address – optional]

Dear Hiring Committee,

I am excited to apply for the **Data Engineer** role (Dallas, TX | Richmond, VA | McLean, VA | Chicago, IL) that requires deep expertise in **AWS Glue, Python, PySpark, and large‑scale ETL pipelines**. Over the past **[X] years** I have designed, built, and operated production‑grade data platforms on AWS that ingest, transform, and serve **billions of records per month** for analytics, machine‑learning, and reporting workloads. My hands‑on experience with Glue jobs, Spark‑SQL, and Python‑based orchestration aligns directly with the technical stack you outlined.

At **[Current Company]**, I led a cross‑functional team to replace a legacy on‑prem Hadoop cluster with a **serverless Glue‑Spark architecture**. By refactoring 12 critical pipelines into modular Glue jobs and leveraging DynamicFrames, we reduced end‑to‑end latency from **12 hours to 45 minutes** and cut infrastructure spend by **38 %**. The solution also introduced automated schema‑evolution handling and data‑quality checkpoints that decreased downstream data‑issue tickets by **62 %**.

Beyond the core technologies, I champion continuous‑integration/continuous‑deployment (CI/CD) for data pipelines using **AWS CodePipeline, GitHub Actions, and Terraform**. This practice ensures that every code change is tested in an isolated dev environment before promotion, guaranteeing reproducibility and compliance—critical for regulated industries operating across multiple states, such as the ones you serve in Texas, Virginia, and Illinois.

I am eager to bring this blend of **AWS‑native engineering, Python/PySpark craftsmanship, and production‑grade ETL discipline** to your team. I look forward to discussing how my background can accelerate your data‑platform roadmap and support the on‑site collaboration you value.

Thank you for your consideration.

Sincerely,

[Your Name]

Tip: Export the above as a PDF (or keep it as plain text if the portal only accepts .doc/.docx). Use a professional font (Calibri 11 pt or Arial 11 pt) and keep margins at 1”.

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2️⃣ Resume – “Data Engineer” Experience (ATS‑friendly)

[Your Name] – Data Engineer
Dallas, TX • (555) 123‑4567 • you@email.com • linkedin.com/in/your‑handle

Professional Experience

Senior Data Engineer – [Current Company], Dallas, TX
Jan 2022 – Present

• Designed & implemented a serverless ETL platform on AWS Glue + PySpark, processing > 2 TB/day (≈ 150 M records) for downstream analytics and ML models.
• Reduced pipeline latency from 12 h → 45 min and cut AWS spend 38 % by migrating from on‑prem Hadoop to Glue‑Spark with DynamicFrames and job bookmarks.
• Built reusable Python libraries for data validation (Great Expectations) and schema‑evolution handling, decreasing data‑quality incidents by 62 %.
• Orchestrated end‑to‑end workflows with AWS Step Functions and Airflow on MWAA, achieving 99.9 % SLA across 30+ daily jobs.
• Implemented CI/CD pipelines (GitHub Actions + Terraform) for Glue job deployment, enabling zero‑downtime releases and audit‑ready change logs.

Data Engineer – [Previous Company], Chicago, IL
Jun 2019 – Dec 2021

• Developed Python‑based ETL scripts that ingested 10 TB/month from S3, RDS, and on‑prem Oracle into a Redshift data warehouse.
• Leveraged PySpark on EMR for batch transformations, achieving 3× speed‑up vs. legacy MapReduce jobs.
• Created partition‑pruning and column‑arbitrage strategies that reduced query cost on Redshift by 45 %.
• Collaborated with data‑science teams to expose feature‑store tables via AWS Glue Data Catalog, supporting real‑time scoring pipelines.

Junior Data Engineer – [Earlier Company], Richmond, VA
Jul 2017 – May 2019

• Maintained ETL pipelines using Python + Pandas and AWS Lambda, processing ≈ 200 M rows per month.
• Automated data‑lineage documentation with AWS Glue Crawlers, improving compliance reporting for GDPR/CCPA.

Technical Skills

Category	Tools / Languages
Cloud	AWS Glue, S3, Redshift, Athena, EMR, Lambda, Step Functions, CloudFormation, Terraform
Programming	Python (3.9+), PySpark, SQL, Bash
Orchestration	Apache Airflow (MWAA), AWS Step Functions
Data Quality	Great Expectations, Deequ
CI/CD	GitHub Actions, CodePipeline, Docker
Version Control	Git (GitHub, Bitbucket)
Other	Kafka (Kinesis), Snowflake (basic), Tableau (visualization)

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3️⃣ Interview Cheat‑Sheet (8 Key Topics)

#	Likely Question	Core Talking‑Points (≈ 30 s each)	One‑Sentence STAR Hook
1	Why AWS Glue over EMR/Databricks?	• Serverless → no cluster mgmt, auto‑scaling • Integrated with Glue Catalog (metadata, schema) • Pay‑per‑second, cost‑effective for bursty workloads	“When we migrated 12 pipelines to Glue, we cut ops overhead by 80 % and saved 38 % on compute.”
2	Explain a complex PySpark transformation you built.	• Used DynamicFrame → DataFrame conversion for Spark‑SQL ops • Window functions for deduplication • UDFs in Python for custom parsing (e.g., JSON‑in‑CSV)	“A nightly job flattened 1 B nested JSON events into a star schema in < 30 min.”
3	How do you ensure data quality in a Glue pipeline?	• Great Expectations + Glue job bookmarks • Automated schema validation via Glue Crawlers • Alerting via SNS/CloudWatch on failure thresholds	“We added a Great Expectations suite that caught 97 % of schema drifts before they hit downstream.”
4	Describe your CI/CD workflow for data pipelines.	• GitHub Actions lint + unit‑test (pytest) • Terraform plan/apply for Glue job resources • Deploy to dev → integration test → promote via CodePipeline	“Our pipeline enables a push‑to‑main to spin up a full dev environment in < 10 min.”
5	Performance tuning: how did you cut latency from 12 h to 45 min?	• Partition pruning, predicate push‑down • Increased DPUs, used spark.sql.shuffle.partitions tuned to 200 • Replaced costly joins with broadcast joins where appropriate	“We rewrote a 5‑stage job to use Glue’s Spark UI to spot skew, then applied salting and broadcast joins.”
6	Handling schema evolution in a data lake.	• Glue Crawlers with versioned tables • Use DynamicFrame applyMapping to add default columns • Store schema history in AWS Glue Catalog + Athena view versioning	“Our pipelines automatically added new columns without breaking downstream reports.”
7	Collaboration with data‑science / BI teams.	• Delivered feature‑store tables via Glue Catalog • Built “data‑as‑code” notebooks (EMR‑Spark) for ad‑hoc analysis • Conducted weekly syncs to prioritize pipeline SLAs	“The feature‑store reduced model‑training latency from 4 h to 30 min.”
8	Security & compliance (e.g., GDPR, CCPA).	• Encryption at rest (S3 SSE‑KMS) & in‑transit (TLS) • IAM role‑based least‑privilege for Glue jobs • Data‑lineage & audit logs via CloudTrail & Glue Data Catalog	“We passed a third‑party audit with zero findings on data‑access controls.”

Quick “STAR” story you can adapt (use for #5, #1, #3, etc.)

• Situation: Legacy Hadoop cluster ran 12 nightly batch jobs, each > 12 h, costing $45 k/mo.
• Task: Migrate to a cost‑effective, low‑latency serverless solution while preserving data‑quality guarantees.
• Action: Designed a modular Glue‑Spark architecture; rewrote jobs using DynamicFrames, added job bookmarks, implemented Great Expectations suites, and set up CI/CD with Terraform.
• Result: End‑to‑end latency dropped to 45 min, compute cost fell 38 %, data‑quality incidents fell 62 %, and the team could release changes daily without manual intervention.

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How to Use This Package

1. Copy the cover letter into a PDF (or the format the employer requests).
2. Paste the resume bullets into your existing resume, ensuring the overall layout stays clean (one‑page for early‑career, two‑page for senior).
3. Print the cheat‑sheet (or keep it on a tablet) for the on‑site interview – you’ll have it handy for quick refreshers before each interview round.

Good luck! 🎉 If you need any tweaks (e.g., tailoring the story to a specific industry or adding more AWS services), just let me know.