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Mid-level, metro data pipeline role with a common engineering title increases applicant competition.
Core PySpark, Databricks, and lakehouse skills translate across industries, making background fit flexible.
Explicit 2–4 years plus required PySpark/Databricks and lakehouse experience enforces strict filtering.
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Own the modernization of MiQ’s data ingestion platform by replacing legacy infrastructure with scalable, declarative pipelines to enable self-serve onboarding at scale.
Improve data quality and governance by embedding automated, contract-enforced standards and observability across Tier-1 and Tier-2 pipelines.
Design cost-efficient storage and compute solutions ensuring data trust, reliability, and scalability aligned with the company’s Data & AI Maturity objectives.
2–4 years software engineering experience, with at least 1 year building data pipelines or platform infrastructure.
Proficient in Python, SQL, and hands-on experience with PySpark or Spark SQL in production.
Experience with distributed computing fundamentals and lakehouse/data warehouse platforms such as Databricks, Snowflake, or BigQuery.
Bachelor’s or Master’s degree in Computer Science, Engineering, or related field.
Experienced in operating within modern data ecosystems focusing on scalable, declarative pipeline architectures and data observability.
Strategic thinker able to proactively design cost and quality controls into data platforms in a fast-evolving AI and data governance context.
Skilled in evaluating and applying diverse data platform technologies beyond default vendors to best fit workload requirements.