AVAkhil Vinayan
Available for new opportunities

Akhil Vinayan.
Data Engineer building reliable pipelines.

I design and optimize scalable ETL/ELT workflows, analytics-ready data systems, and cloud-native platforms - turning raw data into trustworthy insight with Snowflake, Databricks, Dbt, AWS and Airflow.

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github.com/akhilq123 ↗
Portrait of Akhil Vinayan

role

Data Engineer · NielsenIQ

v.2026

3+

Years in data

5

Certifications

10+

Tools & platforms

100+

billions of rows

// About

A growth-oriented engineer at the intersection of data, cloud & quality.

I'm a Data Engineer with hands-on experience building scalable data pipelines, cloud based data solutions, and analytics platforms. My day to-day spans Snowflake, AWS, Apache Airflow, dbt, SQL and Python — designing data warehouses, modeling clean layers, orchestrating workflows, and engineering for data quality across very large datasets.

Before going deep on data engineering I led data quality and QA work, which still shapes how I build today: validation-first pipelines, observable models, and dashboards that teams can actually trust. I collaborate closely with analysts, product and business stakeholders to ship reporting and decision-making systems that move the needle.

/ snapshot

Role
Data Engineer
Focus
ETL/ELT · Warehousing · Quality
Stack
Snowflake · AWS · Airflow · dbt
Based in
Mississauga, ON
Status
Open to relocate

// Experience

From quality engineering to production data systems.

A progression rooted in reliability -> moving from validating data to building the pipelines that produce it.

  1. Data Engineer · NielsenIQ (NIQ)

    May 2024 — Present
    • Build scalable ETL/ELT workflows with Matillion and Apache Airflow.
    • Develop dbt transformation models and tests for analytics-ready layers.
    • Use SQL and Python for validation, performance tuning, and automation.
    • Ship Looker dashboards using LookML and SQL for business KPIs.
    • Manage incidents and backlog in Jira; run scalable processing in Databricks.
    Step 1

  2. QA Engineer / Junior Data Engineer · NielsenIQ (NIQ)

    Apr 2023 — May 2024
    • Monitored deliverables and data quality via Looker dashboards.
    • Performed analyses on large client datasets to surface issues.
    • Managed Jira tickets for defects, discrepancies and pipeline issues.
    • Assisted with validation and testing of client-facing reports & pipelines.
    Step 2

  3. Quality Analyst · Dabler LTD

    Dec 2021 - Mar 2023
    • Requirement analysis, API testing with Postman, and regression support.
    • Test automation support and defect management across Agile sprints.
    • Ad-hoc and exploratory testing for product reliability.
    Step 3

  4. Junior software tester · Datameris

    Apr 2021 - Sep 2021
    • Reviewed requirements and designed test cases for functional and regression testing.
    • Executed manual tests and supported automated testing to validate software quality.
    • Logged and tracked defects in collaboration with developers and QA teams.
    • Maintained test results, documentation, and re-tested fixes to ensure stable releases.
    Step 4

// Skills

The toolkit behind reliable data.

A focused stack across ingestion, transformation, warehousing and quality — selected for production-grade analytics work.

Programming

01
PythonSQLLookmlPySparkpandas

ETL / ELT & Pipelines

02
MatilliondbtApache AirflowLooker

Cloud & Data Platforms

03
SnowflakeAWS S3AWS GlueCloudShellDatabricks

Data Testing & Quality

04
ValidationTest automationRoot-cause analysisLookerLookML

DevOps & Version Control

05
GitGitHubJira

// Certifications & Education

Formally trained. Industry certified.

Certifications

  • 01IBM Data Engineering Professional Certificate
  • 02AWS Certified Cloud Practitioner
  • 03Snowflake SnowPro Associate
  • 04Astronomer — Apache Airflow Fundamentals
  • 05ISTQB Certified Tester

Education

  • Postgraduate Diploma

    Conestoga College

    Canada

  • Bachelor of Engineering

    Anna University

    India

// Selected Work

Areas of impact.

Anonymized case-study summaries drawn from production data engineering work — confidential details omitted.

Scalable ETL/ELT workflows

CASE / 01
Challenge
Ingesting heterogeneous client data at scale with reliable SLAs.
Contribution
Designed orchestrated pipelines with retries, lineage and monitoring.
Outcome
Reduced manual interventions and stabilized daily delivery windows.
MatillionAirflowSnowflake

Analytics-ready dbt models

CASE / 02
Challenge
Inconsistent metrics across reports and dashboards.
Contribution
Built layered models with tests, docs and semantic consistency.
Outcome
A single trusted source for downstream analytics teams.
dbtSQLSnowflake

Data reliability & observability

CASE / 03
Challenge
Silent data issues reaching production dashboards.
Contribution
Implemented validation, anomaly checks and alerting workflows.
Outcome
Faster detection and shorter mean-time-to-resolution on incidents.
dbt testsPythonLooker

Business KPI dashboards

CASE / 04
Challenge
Stakeholders needed self-serve visibility into key metrics.
Contribution
Modeled explores and built dashboards aligned to business logic.
Outcome
Empowered teams to make data-driven decisions independently.
LookerLookMLSQL

Scalable processing with Databricks

CASE / 05
Challenge
Heavy transformation and experimentation on large datasets.
Contribution
Authored notebooks and jobs for distributed processing.
Outcome
Enabled faster iteration on data products and validation flows.
dbtDatabricksPySparkPython

// Contact

Let's build something data-driven.

Open to data engineering roles. Reach out for collaboration, recruiting, or a quick conversation.

/ email

akhilpvinayan@gmail.com
Send an email →GitHub ↗

/ location

Mississauga, Ontario

Willing to relocate

/ availability

Open to opportunities

Full-time · Contract · Remote-friendly