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Protocol Intelligence
Data-driven signals on your job's competitivenessMid-level, popular data-engineer skillset with 3–6 years and metro location yields moderate competition.
Core data engineering skills are transferable across industries, though finance/IR domain exposure adds moderate specificity.
Explicit 3–6 year requirement plus mandatory Databricks, ADF, SQL and PySpark increases filtering strictness.
Job Description
Structured overview of role & requirementsAbout This Role
Own and develop production-grade SQL code and Azure Data Factory (ADF) pipelines across the data platform, ensuring data assets are consistent, well-documented, and governed.
Partner with finance, IR, and portfolio teams to convert undocumented source data into reliable and documented data assets.
Identify automation opportunities, enforce and contribute to engineering standards, and critically evaluate technical design decisions to avoid over-engineering.
Minimum Requirements
3–6 years of experience in data engineering, preferably in a technology-first environment.
Proficiency in strong SQL (T-SQL is a plus) and Python (PySpark and dataframes experience).
Experience with cloud data warehousing platforms like Databricks or Snowflake; Databricks preferred.
Experience with Azure Data Factory, including parameterization and building reusable pipelines.
Ideal Candidate Profile
Comfortable with end-to-end data platform ownership from ingestion to reporting and has a focus on engineering quality and automation.
Experienced in collaborative workflows including peer code review, version control (Git), and documentation.
Has worked in environments emphasizing cloud data platform technologies and is capable of pushing back on unclear requirements or over-engineering.