Data Engineer

MFour Mobile Research•Kansas City, MO

3d•$125,000 - $150,000•Onsite

About The Position

MFour is seeking a Senior Data Engineer to own the technical core of our competitive moat: the multi-source consumer-data pipeline that powers everything DANI and Studio do. You will be responsible for ingesting, cleaning, and unifying behavioral data across all of our data streams into a single, reliable identity graph. This is not a supporting role — this is the data infrastructure that every AI query, every survey fulfillment, and every research deliverable depends on. If the pipeline is wrong, the product is wrong. You own it.

Requirements

5+ years of data engineering experience with clear ownership of production pipelines — not just contribution to them. You have shipped and operated multi-source data systems at scale.
Deep expertise in batch and streaming data pipeline architectures — ingest, transformation, deduplication, and delivery — using tools such as Apache Spark, Kafka, Flink, dbt, Airflow, or equivalents.
Hands-on experience with entity resolution, identity matching, or record linkage across multiple data sources — including the hard cases: conflicting signals, sparse data, and evolving schemas.
Strong command of SQL and at least one general-purpose language (Python strongly preferred) for pipeline development, data validation, and operational tooling.
Cloud data platform experience (AWS or GCP), including managed warehouses (Databricks and Snowflake), object storage, and cloud-native orchestration services.
A rigorous approach to data quality: you define what “clean” means, you build validation into the pipeline, and you treat a silent data error as seriously as a system outage.
Familiarity with consumer data privacy requirements (CCPA, CPRA) and the practical implications for how behavioral data is collected, stored, and processed in a commercial research context.

Responsibilities

Own the end-to-end, multi-source pipeline: ingest, transformation, cleaning, and delivery.
Enforce data-quality standards at ingest — catch schema drift, anomalies, and source failures before they hit downstream systems.
Keep the pipeline fast, scalable, and reliable for a live AI query layer, owning SLAs when upstream sources change.
Own the identity-resolution system that merges behavioral signals into a clean, deduplicated identity graph — the foundation DANI queries against.
Build and refine entity-matching, dedup, and merge logic that resolves conflicting signals, with clear confidence rules.
Partner with the Principal Platform Engineer to optimize data structures and access patterns for DANI — low-latency, high-fidelity, queryable.
Provide freshness and availability guarantees for Survey and Research fulfillment, backed by defined data contracts.
Build monitoring and alerting across the pipeline (freshness, volume anomalies, schema violations, identity drift) so issues surface in minutes, not days.
Own root-cause analysis for incidents — trace failures to the source, document the fix, and harden against recurrence.
Proactively retire technical debt before it becomes a risk.
Enforce data-handling practices that keep behavioral data compliant with MFour's privacy commitments and applicable regulations.
Maintain clear data lineage and retention policies that satisfy internal audits and enterprise-client trust.
Deliver data to Survey and Research fulfillment teams that's available, correctly structured, and on time — with clear ownership when it's not.
Partner with the Product Pod to surface data constraints that shape what DANI can confidently answer.
Document architecture, data contracts, and known failure modes so system knowledge isn't trapped in one person's head.