Sr. Data Engineer, Rev Ops

MiQ•Chicago, IL

About The Position

MiQ is hiring a Senior Data Engineer for Revenue Operations to own the pipelines, data catalog, and integrations that support commercial reporting. This is a hands-on senior individual-contributor role focused on building a clean, well-documented, high-performance data foundation from a complex and changing data environment. The engineer will own data end to end—from ingestion and modeling through governance, quality, reliability, and service—and work within the RevOps analytics team in partnership with Global BI engineering collaborators across the UK and India. The role centers on scalable ETL/ELT pipelines, commercial data modeling, and the structure and governance of Databricks Unity Catalog. It also involves improving data reliability and performance, defining upstream data standards and contracts, integrating commercial systems, and producing documentation for technical and non-technical audiences. The technology stack includes Databricks, Unity Catalog, Delta Lake, Python, SQL, PySpark, Salesforce, Gong, HubSpot, MiQ Sigma, and AWS. The successful candidate is curious, pragmatic, and fundamentals-oriented, able to investigate root causes, make architecture decisions, operate production systems, and communicate technical trade-offs to business stakeholders. The role requires working independently and collaboratively across UK and India time zones.

Requirements

At least 5 years of experience building and operating production data pipelines, with senior-level ownership across ingestion, transformation, quality, governance, and serving.
Ability and judgment to own architecture decisions, not just execute them.
Expert SQL, strong Python, and hands-on Spark/PySpark experience.
Strong data modeling and warehousing fundamentals, including dimensional modeling and snapshot/slowly changing dimension patterns, and ability to define metrics that avoid double-counting.
Modern lakehouse and cloud platform experience.
Data reliability and observability experience, including quality checks and testing, freshness, volume, schema-drift and anomaly monitoring, pipeline health, and data-delivery SLAs.
Production ownership experience, including troubleshooting failures and taking on-call responsibility for business-critical pipelines.
Data governance and security fundamentals, including access control, lineage, and appropriate handling of sensitive or personally identifiable information.
CRM-shaped data and SaaS integration experience, including familiarity with Salesforce data structures and warehouse ELT patterns, API pagination, incremental loads, and schema-drift handling.
Experience improving messy data catalogs, load times, build reliability, and compute cost through better structure and modeling.
Fundamentals-first problem-solving skills, including reasoning about pipeline behavior and debugging from first principles; use of AI tooling as an aid rather than a substitute for understanding.
Technical leadership and communication skills, including setting standards, mentoring as needed, and communicating with technical and business audiences.
Ability to work independently and collaboratively across UK and India time zones, stay effective on tight timelines, and make pragmatic trade-offs.

Nice To Haves

Deep Databricks experience, including Unity Catalog governance, Delta Lake, medallion architecture, incremental ingestion with Auto Loader or Structured Streaming, and tuning with OPTIMIZE, Z-ORDER or liquid clustering, and VACUUM.
AWS experience, including S3 and IAM.
Orchestration and CI/CD experience with tools such as dbt, Airflow, or Databricks/Lakeflow Jobs, as well as Git-based version control and deployment.
Databricks Certified Data Engineer certification at the Associate or Professional level.
Experience with data observability platforms such as Monte Carlo or Great Expectations and with formal data contracts.
Domain knowledge of ad tech, programmatic, or B2B go-to-market data.
BI and semantic-layer exposure, including Sigma, Tableau, or Power BI.
Experience building AI-ready data pipelines, including feature stores, vector databases, or pipelines feeding LLM and GenAI systems.

Responsibilities

Design, build, and maintain scalable ETL/ELT pipelines for RevOps and BI reporting across the commercial data stack.
Own the structure and health of Databricks Unity Catalog, including catalog design, access control, lineage, governance standards, and appropriate handling of sensitive data.
Model commercial data into clean, reusable layers that serve as a trusted source of truth and prevent metric discrepancies.
Build platform reliability through data quality checks, observability, monitoring, and clear data-delivery SLAs; own incident response when pipelines fail.
Optimize pipeline and snapshot performance for load time, reliability, and compute cost, and restructure inefficient catalog areas.
Set materialization strategy, converting expensive or fragile views into governed, efficient tables where appropriate.
Establish data standards and contracts for upstream source schemas, freshness, and ownership to prevent downstream breaking changes.
Integrate first- and third-party commercial sources, including CRM, conversation intelligence, marketing automation, and BI systems, into the warehouse.
Produce and maintain technical and non-technical documentation and translate technical trade-offs into actionable business decisions.
Operate production pipelines, troubleshoot failures, and take on-call responsibility for business-critical pipelines.
Raise engineering standards and mentor teammates as needed.