Data Engineer – Snowflake & Databricks

Below is a ready‑to‑use package you can copy‑paste, tweak with your own details, and submit with your application for the Data Engineer – Snowflake & Databricks role at Cognizant.

It includes:

1. A one‑page résumé (bullet‑point format)
2. A targeted cover‑letter
3. A quick checklist of interview‑ready talking points
4. Optional “extra‑credit” projects you can showcase on GitHub or a personal portfolio

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1️⃣ Résumé (PDF‑ready)

Tip: Keep the file name professional, e.g., John_Doe_Resume_DataEngineer.pdf.

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John Doe

Toronto, ON • john.doe@email.com • +1 416‑555‑1234 • LinkedIn • GitHub

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PROFESSIONAL SUMMARY

Data Engineer with 5 + years of hands‑on experience designing, building, and optimizing cloud‑native ETL/ELT pipelines on Azure. Deep expertise in Snowflake (COPY INTO, tasks, dynamic tables) and Databricks (Spark, PySpark notebooks). Proven track record delivering production‑grade data solutions that enable real‑time analytics, improve data‑quality, and reduce processing costs by up to 30 %. Strong collaborator in Agile environments and champion of CI/CD best practices using Azure DevOps.

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CORE COMPETENCIES

Snowflake	Azure Data Factory	Databricks / Spark	Python (Pandas, Azure SDK)
COPY INTO, Tasks, Streams, Dynamic Tables	Pipelines, Dataflows, Mapping Dataflows, Triggers, Parameterization	Notebooks, Jobs, Delta Lake, Structured Streaming	Pandas, PySpark, Snowflake‑connector‑python, Keyring
Performance tuning (micro‑partitions, clustering keys)	Custom activities (Azure Functions, REST, PowerShell)	CDC, Spark‑SQL, UDFs	Unit testing (pytest), linting (flake8)
Secure data sharing, Row‑Level Security	Integration Runtime, Managed VNet, Private Endpoints	Autoscaling clusters, Job‑cluster vs All‑purpose	Logging (loguru), Monitoring (Azure Monitor)
Data Governance (Tagging, Masking Policies)	CI/CD pipelines (ARM templates, Azure DevOps)	MLflow, Delta‑Lake time‑travel	Docker, Poetry/conda env management

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PROFESSIONAL EXPERIENCE

Senior Data Engineer – XYZ Financial Services, Toronto, ON
Jan 2022 – Present

• Architected & delivered a 12‑pipeline ADF solution (≈ 2 TB/month) that ingested raw transaction data from Azure Event Hub into Snowflake using COPY INTO + Staging tables, cutting latency from 6 h to < 30 min.
• Implemented CDC with Databricks Structured Streaming + Snowflake Streams, achieving near‑real‑time data availability for downstream BI dashboards.
• Optimized Snowflake storage by designing clustering keys and materialized views, reducing query cost by 27 % while improving performance for ad‑hoc analytics.
• Built reusable Python libraries (snowflake_utils, adf_helpers) leveraging Azure SDK, Keyring, and pandas; added unit‑test coverage > 90 % and published to an internal Artifactory.
• Enabled CI/CD for all data‑pipeline code using Azure DevOps (YAML pipelines, ARM templates, Terraform for infra). Automated promotion from DEV → TEST → PROD with zero‑downtime deployments.
• Mentored 4 junior engineers on best practices for Spark‑SQL, Snowflake security, and ADF error‑handling; instituted a weekly “Data‑Ops health‑check” meeting.

Data Engineer – ABC Retail Corp., Vancouver, BC
Jun 2019 – Dec 2021

• Designed end‑to‑end ELT flows in ADF to move POS and e‑commerce data (≈ 1.5 TB/month) into Snowflake; used dynamic tables for incremental loads and tasks for orchestrating dependent pipelines.
• Developed Databricks notebooks for data cleansing (Pandas → PySpark) and enrichment (customer‑level RFM scoring), later packaged as a reusable MLflow model for marketing segmentation.
• Created monitoring dashboards in Power BI using Snowflake’s QUERY_HISTORY and Azure Monitor logs; reduced mean‑time‑to‑detect pipeline failures from 4 h to < 15 min.
• Implemented row‑level security and data masking policies in Snowflake to comply with GDPR and PCI‑DSS requirements.

Junior Data Engineer – TechStart Solutions, Montreal, QC
Jan 2018 – May 2019

• Built ADF copy activities to ingest CSV files from Azure Blob Storage into Azure SQL DW; later migrated to Snowflake as part of a cloud‑migration project.
• Wrote Python scripts (Azure SDK, pandas) for data validation and automated email alerts on schema drift.

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EDUCATION

B.Sc. in Computer Science – University of Toronto, 2017
Relevant coursework: Database Systems, Distributed Computing, Cloud Architecture

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CERTIFICATIONS

• SnowPro Core Certification – Snowflake (2023)
• Microsoft Certified: Azure Data Engineer Associate (DP‑203) – 2022
• Databricks Lakehouse Fundamentals – Databricks (2021)

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TECHNICAL TOOLBOX

• Languages: Python, SQL, Scala (basic)
• Cloud: Azure (ADF, Data Lake, Event Hub, Functions, DevOps), Snowflake, Databricks
• Orchestration: Azure Data Factory, Azure Logic Apps, Airflow (basic)
• CI/CD: Azure DevOps (Repos, Pipelines, Boards), Git, Terraform, ARM templates
• Monitoring: Azure Monitor, Log Analytics, Snowflake Resource Monitors, Datadog (basic)
• Version Control: Git (GitHub, Azure Repos)

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2️⃣ Cover Letter (Tailored to Cognizant)

Subject line (if emailing): Application – Data Engineer – Snowflake & Databricks (John Doe)

File name (if attaching): John_Doe_CoverLetter_DataEngineer.pdf

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[Your Address]
Toronto, ON  M5V 2T6
john.doe@email.com | +1 416‑555‑1234
[Date]

Hiring Manager
Cognizant
[Office Address – if known]

Dear Hiring Manager,

I am excited to submit my application for the **Data Engineer – Snowflake & Databricks** position advertised on Cognizant’s careers portal. With over five years of experience designing, building, and operating large‑scale data pipelines on Azure, Snowflake, and Databricks, I have a proven ability to turn raw data into reliable, analytics‑ready assets that drive business decisions.

At **XYZ Financial Services**, I led the migration of a legacy on‑prem ETL platform to an Azure‑first architecture. Using Azure Data Factory, I built a suite of 12 end‑to‑end pipelines that ingest > 2 TB of transaction data daily, leveraging **COPY INTO**, **dynamic tables**, and **Snowflake tasks** to achieve sub‑30‑minute latency. To support real‑time reporting, I implemented CDC with Databricks Structured Streaming and Snowflake Streams, delivering near‑real‑time data to our BI layer. These efforts reduced processing costs by 27 % and eliminated a 6‑hour nightly batch window.

My day‑to‑day work revolves around **Python‑driven data transformation** (pandas, PySpark) and **robust CI/CD pipelines** built in Azure DevOps. I have authored reusable libraries for Snowflake connectivity, Azure SDK interactions, and secure credential handling with **Keyring**, all covered by comprehensive unit tests and automated deployments. I thrive in Agile environments, collaborating closely with architects, cloud engineers, and business stakeholders to ensure that data solutions are both technically sound and aligned with strategic goals.

Cognizant’s commitment to flexible work models, continuous learning, and delivering high‑impact cloud solutions resonates strongly with my own professional values. I am eager to bring my expertise in Snowflake optimization, Databricks notebook development, and ADF orchestration to your data‑engineering team and help Cognizant’s clients unlock the full value of their data assets.

Thank you for considering my application. I look forward to the opportunity to discuss how my background and skills can contribute to Cognizant’s success.

Sincerely,

John Doe

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3️⃣ Interview‑Ready Talking Points

Theme	Sample Story / Metric
Snowflake performance	“Implemented clustering keys on a 500 M‑row fact table; query runtime dropped from 45 s to 12 s and compute credits saved ~ 15 % per month.”
ADF orchestration	“Built a parent‑child pipeline pattern with dynamic parameters; reduced duplicate code by 40 % and enabled reusable sub‑pipelines for 10+ downstream workloads.”
Databricks CDC	“Used Structured Streaming + Snowflake Streams to capture changes from Event Hub; achieved < 5 min end‑to‑end latency for fraud‑detection alerts.”
CI/CD	“Created Azure DevOps YAML pipelines that automatically run unit tests, linting, and Snowflake schema validation before deploying to PROD; zero‑downtime releases for 6 consecutive months.”
Troubleshooting	“Diagnosed a nightly ADF failure caused by a schema drift; added a pre‑copy schema‑validation step and automated alerting, cutting MTTR from 3 h to 10 min.”
Collaboration	“Partnered with data‑science team to expose a Snowflake‑based feature store; delivered a Spark‑SQL view that reduced model‑training data prep time by 30 %.”
Governance & Security	“Implemented row‑level security and data‑masking policies in Snowflake to meet GDPR; performed quarterly audits with Azure Purview.”

Tip: Use the STAR (Situation, Task, Action, Result) format for each story. Keep the “Result” quantifiable whenever possible.

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4️⃣ Optional “Extra‑Credit” Projects (GitHub Portfolio)

Project	Tech Stack	What It Shows
Real‑time Retail Dashboard	ADF → Event Hub → Databricks Structured Streaming → Snowflake → Power BI	End‑to‑end CDC, low‑latency pipeline, Snowflake dynamic tables
Snowflake Cost‑Optimizer CLI	Python (Click, Snowflake‑connector, pandas)	Automates identification of unused warehouses, recommends clustering keys
ADF‑to‑GitOps Boilerplate	ARM templates, Azure DevOps YAML, Terraform	Demonstrates IaC for ADF pipelines, version‑controlled deployments
Data‑Quality Framework	Pytest, Great Expectations, Azure Functions	Automated data‑validation as a step in ADF pipelines, alerts via Teams

Upload the code, a short README, and a 2‑minute video walkthrough. Mention these projects in your résumé (e.g., “Open‑source project: Real‑time Retail Dashboard – 1.2 k stars on GitHub”).

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5️⃣ Quick Application Checklist

✅	Item
✅	Résumé saved as PDF, ≤ 2 pages, file name First_Last_Resume.pdf
✅	Cover letter saved as PDF, file name First_Last_CoverLetter.pdf
✅	LinkedIn profile up‑to‑date (include “Open to work – Data Engineer” badge)
✅	GitHub portfolio (at least one of the projects above) linked in résumé
✅	Completed Cognizant online application (upload both PDFs)
✅	Follow‑up email (optional) 2‑3 days after submission, referencing the posting ID and expressing continued interest

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Final Thought

You already have the core experience Cognizant is looking for—just frame it in the language of the job posting, back it up with concrete metrics, and showcase a bit of your personal brand (GitHub, certifications). Use the résumé and cover‑letter templates above, tweak the details to reflect your exact achievements, and you’ll be a standout candidate.

Good luck, and feel free to reach out if you’d like a deeper review of any section! 🚀