Senior Data Engineer, Ads Data (AI Native)

This

position

is

listed

on

behalf

of

a

partner

company,

who

manages

all

applications

and

next

steps.

Our

partner

is

looking

for

a

Senior

Data

Engineer,

Ads

Data

(AI

Native)

based

in

Canada

.

Join

a

remote-first

technology

organization

building

innovative

products

that

help

families

stay

connected

and

protected.

As

a

Senior

Data

Engineer,

you

will

play

a

key

role

in

developing

and

scaling

the

data

infrastructure

that

powers

advertising

analytics,

campaign

performance,

and

business

intelligence.

You

will

take

ownership

of

critical

data

pipelines

and

platforms,

improving

their

reliability,

scalability,

performance,

and

cost

efficiency.

Working

closely

with

analysts,

advertising

strategists,

and

commercial

teams,

you

will

transform

complex

datasets

into

trusted,

actionable

insights.

You

will

also

embrace

AI-native

engineering

practices,

leveraging

advanced

AI

tools

to

accelerate

development,

improve

productivity,

and

strengthen

technical

capabilities.

This

is

an

opportunity

to

combine

hands-on

engineering,

technical

leadership,

and

business

impact

within

a

highly

collaborative

international

environment.

Accountabilities

Develop

and

optimize

data

infrastructure

Enhance,

maintain,

and

scale

existing

batch

and

streaming

pipelines,

Delta

Lake

tables,

and

data

workflows

using

Databricks.

Build

business-focused

data

solutions:

Collaborate

with

analysts,

advertising

strategists,

and

sales

teams

to

transform

business

requirements

into

reliable

datasets,

reporting

solutions,

and

actionable

insights.

Manage

data

models

and

metrics:

Develop

and

maintain

dbt

models,

automated

tests,

semantic

layers,

and

standardized

business

metrics

to

ensure

consistency

and

accuracy

across

teams.

Optimize

performance

and

costs:

Improve

Spark

and

Databricks

workloads

through

query

optimization,

partitioning,

cluster

configuration,

and

efficient

resource

allocation.

Manage

cloud

infrastructure:

Maintain

data

platform

infrastructure

using

Terraform

and

AWS,

ensuring

scalable,

secure,

and

reliable

operations.

Strengthen

data

quality

and

governance:

Implement

monitoring,

data

lineage,

SLAs,

alerting,

access

controls,

and

data

contracts

while

ensuring

compliance

with

privacy

and

regulatory

requirements.

Lead

engineering

excellence:

Establish

best

practices

for

version

control,

CI/CD,

testing,

documentation,

and

code

reviews

while

mentoring

colleagues

and

contributing

to

technical

roadmap

decisions.

Integrate

diverse

data

sources:

Consolidate

and

reconcile

information

from

advertising

platforms,

CRM

systems,

external

partners,

and

internal

applications,

including

migrations

from

legacy

reporting

tools.

Advance

AI-native

engineering:

Develop

tools,

workflows,

and

AI

agents

that

provide

LLMs

with

relevant

context

and

accelerate

development,

debugging,

and

system

maintenance.

Drive

continuous

improvement:

Take

ownership

of

foundational

data

pipelines

and

models,

reduce

processing

times

and

infrastructure

spending,

and

increase

self-service

access

to

reliable

business

information.

Requirements

At

least

5

years

of

professional

data

engineering

experience

,

building

and

maintaining

production-grade

data

platforms

at

enterprise

scale.

Advanced

hands-on

expertise

in

Apache

Spark

,

including

PySpark

or

Scala,

with

strong

knowledge

of

partitioning,

joins,

shuffle

behavior,

caching,

skew

management,

and

performance

optimization.

Deep

knowledge

of

Databricks

,

including

Delta

Lake,

Unity

Catalog,

Jobs

and

Workflows,

Databricks

Asset

Bundles,

Structured

Streaming,

and

SQL

Warehouses.

Strong

proficiency

in

SQL

and

Python

or

Scala

for

data

pipeline

development,

automation,

and

query

optimization.

Practical

experience

with

dbt

,

including

data

modeling,

incremental

strategies,

testing,

documentation,

and

CI/CD

integration.

Hands-on

experience

How Jobgether works

• We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.
• We appreciate your interest and wish you the best!
• Why Apply Through Jobgether?

Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.

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