Senior Analytics Data Platform Engineer

SENIOR ANALYTICS DATA PLATFORM ENGINEER

DoubleVerify · New York (Hybrid)

ABOUT DOUBLEVERIFY

DoubleVerify is a leading software platform for digital media measurement, data

and analytics. DV's mission is to be the definitive source of transparency and

data-driven insights into the quality and effectiveness of digital advertising

for the world's largest brands, publishers and digital ad platforms. DV's

technology platform provides advertisers with consistent and unbiased data and

analytics that can be used to optimize the quality and return on their digital

ad investments. Since 2008, DV has helped hundreds of Fortune 500 companies gain

the most from their media spend by delivering best-in-class solutions across the

digital advertising ecosystem, helping to build a better industry. Learn more at

www.doubleverify.com [http://www.doubleverify.com/].

THE TEAM

You will join the Data & Analytics Platform (DAP) team within the Pinnacle

engineering organization. The DAP team owns and operates two data consolidation

platforms — Quantum (contract-driven, next-gen) and Analytics 2.0 (SQL-driven,

legacy) — that ingest, transform, and serve billions of records daily from

social platforms, measurement systems, and third-party partners. The data powers

Looker dashboards, customer-facing reports, and downstream APIs used across

DoubleVerify's product suite.

WHAT YOU'LL DO

* Platform Abstraction & Design: Design and maintain the YAML-based "Contract"

system that allows users to define data entities, transformations, and SLOs

without writing low-level orchestration code.

* Infrastructure as Code (IaC): Develop the translation engine that converts

user contracts into automated dbt models, Airflow DAGs, and Snowflake

objects.

* API Development: Transition the platform from static configuration files to a

dynamic, API-first architecture, enabling programmatic creation of data

artifacts.

* Self-Service Enablement: Build tooling and guardrails that allow business

units to deploy their own data solutions while maintaining global standards

for governance and security.

* Performance & Scale: Optimize the "translation" layer to ensure that

generated jobs are efficient, cost-effective, and leverage the full power of

the Snowflake/dbt stack.

* Developer Experience (DevEx): Act as the "Product Manager" for your platform,

gathering feedback from internal users to simplify the data development

lifecycle.

* Design and build data pipelines that process billions of records a day across

consolidation, semantic, and externalization layers using the DV Internal

Data Platform — a self-service, contract-driven architecture where pipelines

are defined via YAML contracts and automatically deployed to Snowflake,

Airflow, and Looker.

* Develop and extend the Contract Interpreter — a Python library (Pydantic,

Jinja2) that reads contract driven platform based YAML and generates dbt

models, Airflow DAGs, and environment configurations for each deployment

environment (dev, stg, prod).

* Lead new initiatives and integrations with the world's largest social

platforms (YouTube, TikTok, Meta, Snapchat, Reddit, Netflix, etc.) to measure

ad performance end-to-end.

* Build and maintain the semantic layer — design LookML models, explores, and

views that translate consolidated data into customer-ready analytics through

Looker.

* Implement and maintain observability — build monitoring, alerting,

watermarking, and data consistency checks to ensure pipeline reliability and

data freshness at scale.

* Leverage AI agents and tooling — contribute to and use the team's AI agent

workspace (meta-repo with AGENTS.md context files, skills, and MCP

integrations) to accelerate development, automate workflows, and encode

institutional knowledge for AI-assisted engineering.

* Design schema evolution and data migration strategies — manage schema

versioning, backward compatibility, incremental vs. full-refresh deployments,

and large-scale data backfills.

* Work in multi-functional agile teams with end-to-end responsibility for

product development and delivery — from contract definition to

customer-facing data.

* Collaborate directly with engineers from partner platforms on API development

and data integration specifications.

* Train and mentor a team of software engineers.

WHO YOU ARE

REQUIRED

* Bachelor's degree or foreign equivalent in Computer Science, Data

Engineering, or a related field.

* 5+ years of experience in a Data Engineering or related role.

* Strong SQL skills — advanced querying, performance tuning, window functions,

and complex transformations at scale.

* Proficiency in Python — building libraries, data processing scripts, and

automation tooling (experience with Pydantic, Jinja2, or similar templating

frameworks is a plus).

* Deep experience with Snowflake — schema design, Snowpipe, streams, tasks,

materialized views, clustering, and query optimization.

* Experience with dbt (data build tool) — building and maintaining models,

macros, custom materializations, and incremental strategies.

* Experience with orchestration tools — Airflow / Cloud Composer, DAG design,

scheduling, and monitoring.

* Experience with cloud platforms — GCP (GCS, BigQuery, Cloud Composer,

Kubernetes) or equivalent.

* Strong understanding of data warehousing concepts — dimensional modeling,

star/snowflake schemas, slowly changing dimensions, fact/aggregate table

design, and data consistency patterns.

* Experience with CI/CD pipelines — GitLab CI, Flyway migrations, or similar

deployment automation.

* Experience with AI-assisted development tools — Claude Code, Cursor, GitHub

Copilot, or similar AI coding assistants. Experience building or contributing

to AI agent context files (AGENTS.md), skills, or meta-repo patterns is a

strong plus.

PREFERRED

* Experience building or working with contract-driven / configuration-driven

data platforms where pipelines are generated from declarative specifications

(YAML, JSON schemas).

* Experience with Looker / LookML — building semantic models, explores,

aggregate awareness, and dashboard development.

* Experience with Kafka — schema registries, topic management, and streaming

data integration.

* Experience with data quality and observability frameworks — automated

testing, watermarking, data integrity validation, and SLA monitoring.

* Experience with Terraform or infrastructure-as-code for managing cloud

resources.

* Familiarity with data mesh principles — federated data ownership, data

products, and self-service platform design.

The successful candidate’s starting salary will be determined based on a number

of non-discriminating factors, including qualifications for the role, level,

skills, experience, location, and balancing internal equity relative to peers at

DV.

The estimated salary range for this role based on the qualifications set forth

in the job description is between $107,000- $212,000. This role will also be

eligible for bonus/commission (as applicable), equity, and benefits.

The range above is for the expectations as laid out in the job description;

however, we are often open to a wide variety of profiles, and recognize that the

person we hire may be more or less experienced than this job description as

posted.

Not-so-fun fact: Research

[https://urldefense.com/v3/__https://hbr.org/2014/08/why-women-dont-apply-for-jobs-unless-theyre-100-qualified__;!! P6nVbRxDCw3cNFlf! EJszlPs7CwYzNT-NJOg9EZZm8SLk_u9r0_XmHjJQZBRBzKMxMlLzRyQb5e9Gb6uO0F2xTBQvUt-QaHkxF-J6DnOrpLeuZQc$] shows

that while men apply to jobs when they meet an average of 60% of job criteria,

women and other marginalized groups tend to only apply when they check every

box. So if you think you have what it takes but you’re not sure that you check

every box, apply anyway!