Senior Software Engineer (Ads Data Solutions Engineering)
Posted
Summary
Senior Software Engineer on Instacart's Ads Data Solutions team, designing and shipping scalable data-intensive systems for off-platform advertising, cleanrooms, taxonomy automation, and platform foundations while mentoring engineers and driving AI-assisted development.
- The Ads DSE team builds the core capabilities that power Instacart’s off-platform advertising, secure data collaboration (cleanrooms), automated taxonomy management, and their associated platform foundations
- We are hiring a Senior Engineer II (L6) to co-own the technical roadmap, modernize our data solutions infrastructure, scale off-platform integrations and measurement capabilities, extend our cleanroom collaboration capabilities, and automate taxonomy change management workflows
- This role is pivotal for cross-team collaboration, developer productivity, and AI-driven development initiatives
- You are a senior technical leader who designs and ships scalable, data‑intensive systems; drives cross‑team execution; and raises engineering standards
- You’ll partner closely with Product, Data Science, Data Platform, and Governance teams to build reusable abstractions and deliver high‑impact features across our pillars: Off‑platform, Data collaboration (cleanrooms), Ads Taxonomy, Platform, and New Initiatives (AI)
- You will mentor L4/L5 engineers, act as an IC tech lead (no direct reports), and help define a future on‑call model and SLOs
- Lead architecture for reusable, scalable systems across off‑platform workflows, cleanrooms, taxonomy automation, and the Ads Data Solutions Platform
- Evolve core abstractions, testing, CI/CD, observability, and cost efficiency; proactively drive modernization
- Propose and drive initiatives on behalf of engineering management; align stakeholders and manage dependencies
- Off‑platform: expand and harden integrations with external advertising platforms; design privacy-forward activation and identity workflows; reduce reliance on third-party intermediaries; and build measurement ingestion pipelines for performance signals such as reach, frequency, and conversions
- Data collaboration (cleanrooms): extend our cleanroom orchestration and governance tooling (multi-cleanroom support, policy enforcement), develop reusable query templates and partner configurations, optimize pipelines for cost and performance, and scale secure data collaboration deployment and management
- Ads Taxonomy: lead intake automation (UI + backend) and release workflow improvements to reduce manual effort and cycle time
- Platform: assist with platform modernization and developer tooling improvements; contribute to an on‑call/SLO strategy as we mature operations
- Use AI‑assisted tools daily for design, coding, tests, and docs; define team prompt patterns and workflows; identify AI opportunities in internal tools and ops
- Translate ambiguous requirements into pragmatic phases (POC, pilot, production) with clear SLAs/SLOs
- Champion security‑by‑design, data governance, and privacy compliance (e.g., GDPR/CCPA) across pipelines
- Mentor engineers up to L5; set code quality and review standards
- Build cross‑team partnerships (Ads Manager, Measurement Science, Data Platform, Privacy/Legal, and external platforms)
- Lead foundational, high‑visibility initiatives and translate them into measurable impact on revenue, efficiency, and partner adoption
- Co‑own the technical roadmap with scope to define abstractions, standards, and AI‑first workflows across multiple pillars
- Ship impactful features end‑to‑end: direct UID2 activation, Share Manager extensions, taxonomy intake automation, and platform modernization
- Collaborate with strong cross‑functional partners and enjoy occasional trips to our Toronto office to connect with the broader engineering community
- Help define our future on‑call model and operational excellence standards as we scale
- Data pipelines with DBT and Airflow; Spark/Databricks; advanced SQL and performance tuning
- CI/CD, containers, test strategy; observability (metrics/logs/tracing) with tools like Datadog; incident readiness and SLO thinking
- Demonstrated, recent hands-on use of AI‑assisted development tools (e.g., GitHub Copilot, Cursor, OpenAI‑based assistants) to improve speed and quality
- Ability to evaluate when to apply AI vs. deterministic systems; familiarity with evaluation, guardrails, and security/privacy implications in AI usage
- Working knowledge of privacy and compliance practices (GDPR/CCPA) in data workflows
- Snowflake and AWS experience; cost‑aware design; workflow orchestration (e.g., Temporal) and event‑driven patterns
- 8+ years of software engineering experience (typically 10+ for this level) delivering and operating large‑scale backend/data systems; led cross‑team projects and architectural decisions
- Strong in Python and one of Ruby or Go; services/APIs (REST/gRPC); integrations with third‑party APIs (e.g., Meta, Google, The Trade Desk)
- Clear technical writing; stakeholder management; ability to drive consensus and unblock execution
- Experience defining reusable prompts/workflows for team use and contributing to AI‑enabled internal tools or automation
- Experience with ad‑tech/programmatic and measurement integrations; cleanrooms (Snowflake DCR, shares/listings); identity/matching (UID2, RampID) and deconfliction
- Building AI‑powered internal tools (e.g., Slackbots, data assistants)
- Vendor/partner management and external API certification programs
- Snowflake governance patterns, partner data collaboration at scale
- Catalog/taxonomy, entity lineage, or custom hierarchy systems
- Direct experience with UID2/TTD integrations, Roku/Pubmatic/NBCU, or Meta Advanced Analytics/Google ADH pipelines