Sr. Data Engineer, Rev Ops

Job Description Job Description Role: Senior Data Engineer, Revenue Operations Location: Chicago/US THE OPPORTUNITY Revenue Operations at MiQ runs on data, and we are looking for a Senior Data Engineer to own the pipelines, catalog, and integrations that power our commercial reporting. This is a hands-on, senior individual-contributor role for an engineer who can turn a messy, fast-moving data environment into a clean, well-documented, high-performance foundation. You will own end to end the data that leadership, sales, and finance rely on, from ingestion and modeling through governance, quality, reliability, and service. You will work within our RevOps analytics team and partner directly with the Global BI engineering group, including collaborators across the UK and India. We want someone who codes from fundamentals, thinks in systems, and is genuinely curious, the kind of engineer who is not afraid to open the can of worms to fix the root cause. WHAT YOU'LL OWN Design, build, and maintain the scalable ETL/ELT pipelines that feed RevOps and BI reporting across our commercial data stack. Own the structure and health of our Databricks Unity Catalog, including catalog design, access control, lineage, data 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 the metric discrepancies that erode confidence in reporting. Build reliability into the platform through data quality checks, observability and monitoring, and clear data-delivery SLAs, and own incident response when pipelines break. Continuously optimize pipeline and snapshot performance for load time, reliability, and compute cost, and restructure messy catalog areas into efficient designs. Set the materialization strategy across the environment, converting expensive or fragile views into governed, efficient tables where it makes sense. Establish data standards and contracts for how upstream sources are delivered and documented (schema, freshness, ownership) to prevent breaking changes downstream. Integrate first and third-party commercial sources (CRM, conversation intelligence, marketing automation, BI) into the warehouse for full-funnel visibility. Produce and maintain documentation for both technical and non-technical audiences, and translate technical trade-offs into decisions the business can act on. Our stack: Databricks, Unity Catalog, Delta Lake, Python, SQL, PySpark, Salesforce, Gong, HubSpot, MiQ Sigma,  AWS. WHAT YOU'LL BRING The must-haves. If you meet most of these and are strong where it counts, we want to talk. 5+ years building and operating production data pipelines, with senior-level ownership across ingestion, transformation, quality, governance, and serving, and the judgment to own architecture decisions rather than just execute them. Expert SQL and strong Python , with hands-on Spark / PySpark. Strong data modeling and warehousing fundamentals , including dimensional modeling and snapshot / slowly-changing-dimension patterns, with the rigor to define metrics that avoid double-counting. Modern lakehouse and cloud platform experience. Deep Databricks (Unity Catalog governance, Delta Lake, medallion architecture, incremental ingestion via Auto Loader or Structured Streaming, and tuning with OPTIMIZE, Z-ORDER or liquid clustering, and VACUUM) and AWS (S3, IAM) are a strong plus. Orchestration and CI/CD , using tools such as dbt, Airflow, or Databricks / Lakeflow Jobs, with Git-based version control and deployment. Data reliability and observability , including quality checks and testing, monitoring for freshness, volume, schema drift, and anomalies, and ownership of pipeline health and data-delivery SLAs. Production ownership , comfortable operating what you build, 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 PII data. CRM-shaped data and SaaS integration experience. Familiarity with Salesforce data structures, and integrating SaaS sources into a warehouse layer using ELT patterns, API pagination, incremental loads, and schema-drift handling. A track record cleaning up messy data catalogs , improving load times, build reliability, and compute cost through better structure and modeling. Fundamentals-first problem solving. You can reason about pipeline behavior and debug from first principles, and you use AI tooling as an accelerant rather than a substitute for understanding. Technical leadership and communication. You raise the engineering bar, set standards, and mentor teammates as needed, and you move between technical and business audiences without losing either. Comfortable on tight timelines, pragmatic about trade-offs, able to work independently and across UK and India time zones. BONUS POINTS Databricks Certified Data Engineer (Associate or Professional). Experience with data observability platforms (such as Monte Carlo or Great Expectations) and formal data contracts. Domain knowledge of ad tech, programmatic, or B2B go-to-market data. BI and semantic-layer exposure (Sigma, Tableau, Power BI). Building AI-ready data pipelines, including feature stores, vector databases, or feeding LLM and GenAI systems. YOU'LL THRIVE HERE IF YOU ARE Curious by default, and unafraid to dig into the hard, tangled problems. Pragmatic about trade-offs and focused on outcomes over polish for its own sake. A collaborative team player who stays focused on solutions and shared outcomes. WHO WE ARE We’re MiQ, a programmatic media partner for marketers and agencies. We connect data from multiple sources to do interesting, exciting, business-problem-solving things for our clients. We’re experts in data science, analytics and programmatic trading, and we’re al