JOB:  Data Engineer SQL /BigQuery - Malmo (274226)

Background

Our team sits within IKEA Group Digital’s Data & Analytics organization. We build and operate modern data platforms that enable reliable, governed data consumption across the business, using agile delivery methods and modern software practices (DevOps, CI/CD) to deliver at pace while maintaining resilient, production-quality systems. We place a strong emphasis on balancing speed with long-term engineering quality to avoid technical debt and ensure sustainable implementations.
 
The assignment supports an Econometrics initiative focused on Media Mix Modelling (MMM). The consultant will design, build, and harden production-grade data pipelines that ingest, transform, validate, document, and serve MMM-ready datasets. The MMM model itself is executed by an external agency; the consultant will primarily collaborate with other data engineers in our product team, partnering with analytics as needed. 

Success means delivering trusted, timely datasets with transparent lineage, automated data quality checks and monitoring, and cost-aware, maintainable pipelines aligned to our platform standards and outcome-based ways of working.
 
Day-to-day, the consultant will contribute to schema and data model design, implement transformation pipelines that can evolve with changing requirements while maintaining stable operations, and apply CI/CD practices for efficient, safe delivery.

They will implement data validation and data quality dashboards to sustain high confidence in downstream decisions and analytics. We expect clear, proactive communication and collaboration consistent with our product-oriented, cross-functional setup and IKEA leadership behaviors and values.
 
The ideal profile combines strong SQL with at least one general-purpose language (e.g., Python, Java, Scala, or Go), and familiarity with distributed data processing and streaming technologies (e.g., Spark, Beam, Kafka).

Experience working in agile/DevOps teams, comfort with CI/CD workflows, and a mindset that balances rapid delivery with robust engineering for data quality at scale are essential.

The scope of the consultant services is to assist IKEA in

Designing, building, and hardening production-grade data pipelines and data models that provide trusted inputs to our Econometrics/Media Mix Modelling (MMM) program.

The MMM model itself is operated by an external agency; the consultant will primarily collaborate with IKEA data engineers to deliver reliable, documented, and observable data assets at scale.
 
Objectives and outcomes

- Deliver robust ingestion and transformation across heterogeneous sources into MMM-ready datasets in BigQuery.
- Define and evolve clear schemas and versioned data models that accommodate changing business requirements while keeping operations stable.
- Implement automated data validation, quality monitoring, and alerting to maintain high trust in downstream analytics.
- Ensure cost-aware, resilient pipelines with CI/CD, testing, and documentation suitable for audit and handover.
 
Ways of working

- Product-oriented, iterative delivery with clear acceptance criteria and measurable outcomes.
- Collaboration mainly with data engineers; partner with analytics as needed. The external MMM agency will consume the engineered datasets.
- Tech stack: Google BigQuery as primary warehouse
 
Deliverables

- Production-ready pipelines and models for prioritized MMM datasets in BigQuery.
- Data quality framework (rules, monitors, alerts) and supporting dashboards.
- Technical documentation (schemas, lineage/metadata, data contracts), runbooks, and knowledge transfer materials.

· Requirements:

Desired knowledge, experience, competence, skills etc
 
Technical

- Strong SQL (advanced joins, window functions, performance tuning) and solid software engineering fundamentals (version control with Git, code reviews, testing).
- Hands-on experience building production-grade data pipelines and models in Google BigQuery.
- Experience with Dataform (or similar and/or willingness to quickly become proficient)

and CI/CD practices for data (environments, automated tests, incremental deployments).
- Data quality and observability: implement validation rules, monitors, alerts; define and meet data SLOs (freshness, completeness, accuracy).
- Proficiency in at least one language for data engineering (Python, Java, or Scala).
- Understanding of data modeling patterns for analytics (star/snowflake, slowly changing dimensions, partitioning, clustering).
- Familiarity with Agile ways of working (iterative delivery, clear acceptance criteria, outcome focus)

and DevOps practices applicable to data.
- Working knowledge of data privacy/compliance principles (e.g., GDPR basics, PII handling, access controls) as applied to analytics datasets.
 
Soft skills

- Clear, concise communicator who can explain technical trade-offs to engineers and analytics partners; strong documentation habits.
- Quality-first mindset with pragmatism: balances speed with maintainability and cost efficiency.
- Collaboration and ownership: proactive in raising risks, managing dependencies, and driving issues to resolution.
- Comfortable working remote-first with asynchronous collaboration and predictable availability for ceremonies.

Bonus to have:

Technical

- Deeper Google Cloud ecosystem familiarity around BigQuery (e.g., IAM, cost optimization, scheduling/orchestration) and strengthening Dataform automation/coverage.
- Experience with marketing/media datasets and concepts relevant to MMM (campaign hierarchies, spend, impressions, conversions, attribution windows).
- Exposure to event/stream processing or large-scale batch frameworks (e.g., Kafka, Spark, Beam) where appropriate to the use case.
- Data governance and metadata: lineage, data contracts, cataloging, and audit readiness.
- Security-by-design practices in data engineering (least privilege, secrets management).
- Basic understanding of econometrics/MMM workflows and how data design affects model readiness.


What 4 things from the box above are most important?

1) Proven BigQuery + strong SQL, building production-grade pipelines and models
- Why: Our stack centers on BigQuery; we need someone who can deliver robust, performant, cost-aware pipelines end to end.
 
2) Data quality and observability(automated validation, monitors/alerts, meeting SLOs)
- Why: MMM depends on trustworthy inputs. Quality gates and monitoring are critical to model reliability and stakeholder confidence.
 
3) CI/CD for data with Dataform (or rapid ramp-up), including testing and incremental deployments
- Why: We need safe, repeatable releases and higher coverage of Dataform automation to move fast without sacrificing stability.

o 4-7 years experience