ABOUT THE DOYLE GROUP
The Doyle Group is a proven partner for Placement and Consulting services, headquartered in Denver, CO. Our core mission is to forge genuine partnerships with our clients who seek strategic talent solutions and to assist highly skilled candidates looking for their next career opportunity. With over 30 years of industry experience, our consultative approach allows us to provide a higher level of guidance and insight, empowering our clients to secure top IT talent that fits seamlessly into their team and culture. We look forward to collaborating to help you achieve your career goals.
POSITION SUMMARY
Our client is a high-growth, private equity-backed marketing and advertising technology agency that delivers direct-response campaigns across TV, audio, digital, and direct mail, with a rapidly expanding data and analytics function supporting both its legacy media business and a growing slate of digital campaigns.
This is an individual-contributor role on a data engineering team of roughly six people whose experience ranges from just past entry-level to highly experienced. The team is looking to backfill an open seat and keep growing its data engineering bench. The role is centered on building and maintaining the pipelines, connectors, and integrations the rest of the business depends on for reporting, analytics, and increasingly, AI-driven workflows — roughly 70% hands-on pipeline and platform engineering and 30% direct partnership with analysts, media buying, and development teams.
The ideal candidate wants real, hands-on ownership from day one, is comfortable working on the same tech stack the team's senior engineers and technical lead use, and is open to learning the media and advertising domain. There is no leadership or management expectation in this role, and no assumption of prior media-industry background — strong fundamentals and the right mindset matter more.
This role is based near Shelton, CT, with an expectation of 2-3 days onsite per week; some flexibility exists depending on experience.
This is a full-time, direct-hire position. Candidates must be authorized to work in the United States without current or future visa sponsorship.
RESPONSIBILITIES
- Design, build, and maintain scalable, reliable, idempotent data pipelines from raw ingestion through modeled, analysis-ready tables.
- Own pieces of the ETL/ELT lifecycle: extraction, transformation, loading into Snowflake, orchestration, scheduling, retries, alerting, and backfills.
- Integrate external systems via REST APIs, including ad platforms, CRMs, call tracking, and vendor feeds, handling OAuth 2.0, pagination, rate limits, schema drift, and partial failures without losing or duplicating data.
- Configure and manage ELT connectors such as Airbyte and Funnel.io, including sync scheduling, schema-change handling, and monitoring.
- Support AWS infrastructure as code using Terraform or CloudFormation so environments stay versioned and reproducible.
- Contribute to core Snowflake functionality: schema design, dimensional modeling, partitioning and clustering, warehouse sizing, and query performance tuning.
- Build data quality and observability checks into pipelines: freshness and volume checks, row-level validation, reconciliation, and alerting.
- Help monitor and optimize platform cost, including Snowflake credit consumption and AWS spend, treating cost as part of the design process rather than an afterthought.
- Partner directly with analytics, media buying, account, and finance teams to understand data needs and resolve application and data issues.
- Collaborate with developers on code review, paired programming, bug fixes, and shared engineering standards.
- Contribute to AI/agent-ready processes, helping expose data sources as reliable, self-service tools for internal AI and agent workflows.
- Support hiring, onboarding, and mentoring as the team continues to grow.
MINIMUM EXPERIENCE
- 3+ years of professional experience in a data engineering, analytics engineering, or backend data-focused role.
- Strong Python for production data work, including:
- Clean, modular, testable code; virtual environments and dependency management; error handling and logging
- Building and consuming REST APIs (FastAPI, Flask, or similar)
- Working within existing frameworks (e.g., Django) or writing custom connectors and pipelines
- Advanced SQL, including CTEs, window functions, and query optimization, in Snowflake or a comparable cloud warehouse.
- Comprehensive hands-on AWS experience:
- Core comfort with S3, Lambda, and IAM
- Familiarity with several of: Glue, Step Functions, EventBridge, ECS/Fargate, Secrets Manager, CloudWatch, RDS, and SQS/SNS
- Working experience with infrastructure as code (Terraform or AWS CloudFormation), including an understanding of state, modules, and managing multiple environments.
- Experience with a managed ELT/ETL / data integration platform such as Airbyte or Funnel.io.
- Pipeline orchestration experience with at least one of Temporal, Prefect, or AWS Step Functions.
- Solid understanding of database fundamentals: normalization, indexing, transactions, constraints, and data modeling trade-offs.
- A critical thinker comfortable working with unstructured, messy, and inconsistent datasets.
- Clear communication with non-technical stakeholders, plus strong attention to detail, project management, and organizational skills.
- Bachelor's degree in Computer Science, Engineering, Mathematics, Statistics, or a related field, or equivalent hands-on experience.
- Must be legally authorized to work in the United States without sponsorship now or in the future.
ADDITIONAL PLUS
- Background in media, advertising, or direct-response marketing, or an adjacent domain such as CRM, sales enablement, or a DSP/media platform
- Some hands-on exposure to LLMs or AI agents, even informal or productivity-focused use
- Experience working across a high-volume connector environment (dozens of integrations)
- Comfort operating in a fast-paced, deliverable-driven, daily-standup team culture
- Genuine interest in growing toward more senior or technical-lead scope over time
