Our data team has expertise across engineering, analysis, architecture, modeling, machine learning, artificial intelligence, and data science. This discipline is responsible for transforming raw data into actionable insights, building robust data infrastructures, and enabling data‑driven decision‑making and innovation through advanced analytics and predictive modeling.

As a Data Engineer at Endava you will:

Data Engineering & Architecture

- Design, develop, and maintain scalable ETL/ELT pipelines for data ingestion, transformation, validation, and delivery.
- Develop reusable, modular, and configuration‑driven data processing solutions.
- Contribute to technical architecture and evaluate solutions based on scalability, performance, reliability, and maintainability.
- Design and optimize data models, SQL queries, stored procedures, and data processing workflows.

Workflow Orchestration & Data Quality

- Design and maintain Apache Airflow workflows, including scheduling, dependencies, retries, monitoring, and error handling.
- Develop automated data validation and quality checks using DBT and SQL.
- Implement appropriate monitoring, alerting, and observability for critical data pipelines.
- Troubleshoot and resolve complex data processing and quality issues.
- Develop data solutions using AWS and cloud‑based data warehouse technologies such as Snowflake.
- Work with cloud services for data storage, processing, compute, messaging, and serverless workloads.
- Support data platforms across development, QA, staging, and production environments.
- Optimize data workloads for performance, scalability, reliability, and cost.

CI/CD & Production Support

- Develop and maintain CI/CD pipelines for data engineering applications and infrastructure.
- Apply Infrastructure‑as‑Code practices using Terraform or equivalent technologies.
- Support production deployments, incident resolution, root cause analysis, and continuous improvement.
- Maintain technical documentation, operational runbooks, and troubleshooting guides.

Technical Leadership

- Take ownership of complex engineering initiatives from design through production.
- Conduct code reviews and promote engineering best practices.
- Mentor junior and mid‑level engineers and provide technical guidance.
- Collaborate with architecture, DevOps, product, and other technology teams.

Our data team has expertise across engineering, analysis, architecture, modeling, machine learning, artificial intelligence, and data science. This discipline is responsible for transforming raw data into actionable insights, building robust data infrastructures, and enabling data‑driven decision‑making and innovation through advanced analytics and predictive modeling.

As a Data Engineer at Endava you will:

- Data Engineering & Architecture

- Design, develop, and maintain scalable ETL/ELT pipelines for data ingestion, transformation, validation, and delivery.
- Develop reusable, modular, and configuration‑driven data processing solutions.
- Contribute to technical architecture and evaluate solutions based on scalability, performance, reliability, and maintainability.
- Design and optimize data models, SQL queries, stored procedures, and data processing workflows.

- Workflow Orchestration & Data Quality

- Design and maintain Apache Airflow workflows, including scheduling, dependencies, retries, monitoring, and error handling.
- Develop automated data validation and quality checks using DBT and SQL.
- Implement appropriate monitoring, alerting, and observability for critical data pipelines.
- Troubleshoot and resolve complex data processing and quality issues.

- Cloud & Platform Engineering

- Develop data solutions using AWS and cloud‑based data warehouse technologies such as Snowflake.
- Work with cloud services for data storage, processing, compute, messaging, and serverless workloads.
- Support data platforms across development, QA, staging, and production environments.
- Optimize data workloads for performance, scalability, reliability, and cost.

- CI/CD & Production Support

- Develop and maintain CI/CD pipelines for data engineering applications and infrastructure.
- Apply Infrastructure‑as‑Code practices using Terraform or equivalent technologies.
- Support production deployments, incident resolution, root cause analysis, and continuous improvement.
- Maintain technical documentation, operational runbooks, and troubleshooting guides.

- Technical Leadership

- Take ownership of complex engineering initiatives from design through production.
- Conduct code reviews and promote engineering best practices.
- Mentor junior and mid‑level engineers and provide technical guidance.
- Collaborate with architecture, DevOps, product, and other technology teams.

- Programming: Python, SQL
- Orchestration: Apache Airflow or equivalent
- Data Transformation: DBT, SQL, PySpark
- Data Warehouse: Snowflake or equivalent cloud data warehouse
- Cloud: AWS or equivalent cloud platform
- AWS Services: S3, EMR, Lambda, SNS/SQS or equivalent
- CI/CD: Jenkins, GitHub, GitHub Actions, or equivalent

Mandatory Qualifications

- 5+ years of professional experience in data engineering or a related software engineering role.
- Strong hands‑on experience building and supporting production‑grade data pipelines.
- Strong knowledge of Apache Airflow, including DAG development, scheduling, dependencies, retries, and monitoring.
- Strong experience with Snowflake or equivalent cloud data warehouse technologies.
- Proven experience with DBT.
- Solid experience with AWS or another major cloud platform.
- Experience with CI/CD and Infrastructure‑as‑Code practices.
- Strong programming skills in Python and SQL.
- Demonstrated experience troubleshooting complex production issues and performing root cause analysis.
- Ability to independently own complex technical initiatives from design through implementation and production.
- Strong communication, problem‑solving, and technical documentation skills.

Preferred Qualifications

- Experience with large‑scale data processing.
- Experience with PySpark and EMR.
- Experience mentoring engineers and contributing to technical standards and architecture.

- Finance: Competitive salary package, value‑based recognition awards, referral bonus;
- Career Development: Career coaching, global career opportunities, non‑linear career paths,



internal development programmes for management and technical leadership;
- Learning Opportunities: Complex projects, rotations, internal tech communities, training, certifications, coaching, online learning platforms subscriptions, pass‑it‑on sessions, workshops, conferences;
- Work‑Life Balance: Hybrid work and versátil working hours, employee assistance programme;
- Health: Global internal wellbeing programme, access to wellbeing apps;
- Community: Global internal tech communities, hobby clubs and interest groups, inclusion and diversity programmes, events and celebrations.

At Endava, we’re committed to creating an open, inclusive, and respectful environment where everyone feels safe, valued, and empowered to be their best. We welcome applications from people of all backgrounds, experiences, and perspectives—because we know that inclusive teams help us deliver smarter, more innovative solutions for our customers. Hiring decisions are based on merit, skills, qualifications, and potential. If you need adjustments or support during the recruitment process, please let us know.

AgileEngine is an Inc. 5000 company that creates award‑winning software for Fortune 500 brands and trailblazing startups across 17+ industries. We rank among the leaders in areas like application development and AI/ML, and our people‑first culture has earned us multiple Best Place to Work awards.

WHY JOIN US

If you're looking for a place to grow, make an impact, and work with people who care, we'd love to meet you!

ABOUT THE ROLE

We are looking for a Mid‑level Data Engineer to build and maintain dbt pipelines and Snowflake data marts behind customer‑facing reporting, spend analytics, and AI features.

The mandatory requirements are solid dbt experience (modeling, tests, macros, and incremental models), ingestion and orchestration tools such as Airbyte or Prefect, 3+ years in data or analytics engineering on production pipelines, strong SQL and Python, dimensional modeling, Snowflake or a similar cloud warehouse, and an upper‑intermediate English level.

WHAT YOU WILL DO

- Build and maintain the dbt pipelines, data marts, and warehouse models that serve in‑app reporting, spend analytics, AI features, and internal analytics, treating them as production systems.
- Translate complicated procurement business logic into maintainable, well‑tested pipeline code, and notice when a model is valid‑looking but wrong.
- Build models within our data mart architecture, following the chapter's conventions for grain, conformed dimensions, and incremental patterns, and propose improvements when you find them.
- Contribute to and help buildout the semantic layer that our internal MCP servers and AI agents query, so metrics and dimensions are defined once and answered consistently.
- Maintain the boundary between PII and agent/reporting‑safe data.
- Build and maintain the FastAPI services that expose reporting marts to customers.
- Build and maintain ingestion pipelines (Airbyte, OneSchema, S3) into the warehouse, and support our Forward Deployed Engineering function in bringing customer data into our systems.
- Implement data quality checks: freshness, schema and reconciliation tests, and alerting, so problems surface before customers find them.
- Implement metric definitions faithfully, and flag conflicting or ambiguous definitions to the chapter rather than guessing.
- Keep documentation and lineage current so that humans and AI agents can both find and trust what's in the warehouse.
- Tune Snowflake performance and cost within the models you build.
- Use agentic coding tools in your daily work, within the guardrails (tests, conventions, review patterns) the team maintains, and understand why those guardrails exist.
- Share what you learn through code review and team discussion.
- Communicate clearly with engineering, product, and customer‑facing teams about what the data means and what it can and can't do.

MUST HAVES

- 3+ years in analytics engineering, data engineering, or a closely related role, including hands‑on work on production data pipelines that other systems or customers depended on.
- Solid dbt experience: modeling, tests, macros, and incremental models.
- Strong SQL and working Python.
- Data modeling fundamentals: dimensional modeling, data marts, grain and conformance.
- Experience with a modern cloud warehouse (Snowflake preferred).
- Experience with ingestion tooling and orchestration (Airbyte, Prefect, or equivalents).
- Familiarity with agentic coding tools and an understanding of why guardrails around their output matter.
- Deep curiosity about the business: you want to understand what the data means and how customers use it, not just how it flows.
- Software engineering discipline for analytics code: Git, code review, CI/CD.
- Can take a multi‑sprint piece of work from spec to production with normal oversight, and can explain tradeoffs to technical and non‑technical stakeholders.

NICE TO HAVES

- Experience building or maintaining API services on top of warehouse data (FastAPI or similar).
- Experience with a semantic layer or governed metric definitions that multiple consumers rely on, including for LLM‑driven or agent‑driven querying.
- Experience exposing warehouse data to customers through embedded analytics or customer‑facing APIs.
- Experience with data observability tooling.
- Experience supporting customer data onboarding or implementation teams.
- Experience migrating legacy ingestion or orchestration patterns such as Boomi or AWS Data Pipeline to modern managed or Kubernetes‑based solutions.
- Knowledge of HIPAA, GDPR, FDA‑related controls, least‑privilege access, separation of duties, and audit‑ready operational practices.
- Experience deploying data pipelines across multiple regions and managing PII or sensitive data.
- Prior experience in enterprise software and startups.

PERKS AND BENEFITS

- Professional growth: Mentorship, TechTalks, and personalized growth roadmaps.
- Competitive compensation:



USD‑based pay with education, fitness, and team activity budgets.
- Exciting projects: Modern solutions with Fortune 500 and top product companies.
- Flextime: Flexible schedule with remote and office options.

Data Engineer (Marketing‑oriented) | Remote for Marketing Agency for Education

Our client is a fast‑growing performance marketing agency specializing in mission‑driven organizations across higher education and nonprofit sectors. Since launching in 2020, the agency has built a strong reputation for combining strategic thinking, creative execution, and data‑driven marketing to help universities and organizations achieve measurable growth.

Company Overview:

Our client is a fast‑growing performance marketing agency specializing in mission‑driven organizations across higher education and nonprofit sectors. Since launching in 2020, the agency has built a strong reputation for combining strategic thinking, creative execution, and data‑driven marketing to help universities and organizations achieve measurable growth.

Your Role:

The Senior Data Engineer sits at the center of the agency's Data and Analytics function, connecting every paid media platform and CRM the agency touches into a single, reliable source of truth. You'll own the full path data takes: from a client's ad accounts and CRM, through Adverity, into a BigQuery warehouse, through modular dbt/Dataform transformations, and out to the Looker Studio dashboards and automated Google Sheets reports the team and its partners rely on daily. Reporting to the Data and Analytics Manager, you're joining at a pivotal moment — the agency is scaling its roster of education and nonprofit partners, and this role is instrumental in evolving the automated reporting suite that makes that growth possible without sacrificing data quality or client trust.

You’ll:

- Own data ingestion & pipeline architecture — connect and maintain API pipelines from paid media platforms (Meta, Google Ads, LinkedIn, TikTok, programmatic) into Adverity, build custom Python connectors for anything not natively supported, and architect scalable pipelines from source system through to dashboard
- Own CRM integration — build and maintain reliable CRM data connections (Slate, Salesforce, HubSpot) so lead and enrollment data flows cleanly into the warehouse
- Own the data warehouse — route, optimize, and store raw and processed data in Google BigQuery; design efficient schemas for complex, multi‑source marketing data (granularity, normalization, slowly changing dimensions); manage partitioning, clustering, and storage strategy to control query cost and performance
- Own transformation & mapping — build SQL schemas that map, blend, and organize multi‑channel data accurately for each client, in modular, version‑controlled, documented dbt or Dataform pipelines, with standardized naming conventions and metric definitions across platforms and clients
- Own CI/CD & data segregation — maintain CI/CD pipelines (GitHub Actions, automated testing) for robust, version‑controlled releases, and enforce strict data segregation across multi‑client environments
- Enable the reporting layer — create clean, performant data views built specifically for fast Looker Studio dashboards and automated Google Sheets reports, and partner with the Data and Analytics Manager and client teams to translate reporting requirements into warehouse models
- Drive data quality — monitor pipeline health, resolve API breaks, minimize latency, implement validation frameworks and automated testing, troubleshoot discrepancies to root cause across source/warehouse/BI layers, and proactively flag data quality risks to leadership

You Bring:

- 5+ years of hands‑on data engineering experience, ideally in a marketing or agency environment
- Strong experience configuring datastreams, transformations, and destinations in Adverity or a comparable ETL platform (Fivetran, Supermetrics, Funnel)
- Intermediate to advanced SQL, with proven BigQuery experience in transformation, partitioning, and modeling
- Expertise in dbt or Dataform, managing modular, version‑controlled, documented SQL transformation pipelines
- Python proficiency for custom API connections and pipeline automation
- Experience with CRM data flows and integrations (Slate, Salesforce, and/or HubSpot)
- A deep understanding of marketing metrics (ROAS, CPA, CAC) and paid platform schemas, with a proven track record of structuring data to optimize Looker Studio performance
- Proficiency with Git and collaborative development workflows, and proven ability to manage multi‑client data environments with strict data segregation
- An analytical, solution‑driven mindset with a growth orientation, and strong communication skills that turn complex data workflows into clear narratives for non‑technical audiences — education marketing experience (traditional or online) is especially valued

Bonus Points:

- Experience integrating AI‑driven insights or machine learning models into marketing data pipelines to sharpen predictive analytics and data utility
- Familiarity using AI or LLM tools to optimize performance forecasting, automated audience segmentation, or marketing workflows
- Experience connecting, managing, and maintaining APIs for generative AI platforms within a marketing data architecture
- Experience using AI/ML‑driven anomaly detection to monitor pipelines and proactively flag data quality issues

What’s Offered:

- Remote role with the flexibility that comes with a distributed, async‑friendly team
- Competitive compensation based on experience and location
- A pivotal, high‑visibility seat on a scaling Data & Analytics team, with real ownership over the agency's reporting infrastructure
- The chance to build the data backbone for an agency whose partners are education and nonprofit organizations working to build a better world
- A culture built around finding a better way, playing as a team of A‑players, and treating results as the engine rather than the goal
- Direct impact: the pipelines and models you build directly shape how partners see (and trust) their own performance data

Interview Process:

- Application Review (resume + a few questions)
- Screening Call with Atomic HR
- Hiring Team Interview (walkthrough of a pipeline or data model you've built)
- Technical Assessment (a real‑world data engineering case study)
- Final Interview with company leadership

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📌 Senior Ai Data & Bi Engineer (Rosario)
🏢 The Ward Law Group
📍 Rosario