Database Administrator - Data Governance

This is a hands on data engineering role with database administration and governance ownership. You will build and operate the pipelines that move data into our platform, model it so the business can trust the numbers, implement the governance and access controls that protect it, and support the production databases underneath it all. Our environment includes PostgreSQL, Amazon Redshift, API based sources, and flat files in S3. You will work across development, QA, and production environments alongside Development, Operations, and Data Engineering. This is a role for someone who has personally built and debugged these systems, not only designed or overseen them. What You Will Own Data integration and incremental loading Build and maintain batch and incremental pipelines in Python and PySpark across Postgres, Redshift, API based sources, and flat files in S3Implement change data capture, including identifying changed records where source systems provide no reliable modification timestamp, using log based CDC, hash comparison, or snapshot differencingDesign idempotent pipelines that restart cleanly after partial loads, with checkpoint management and duplicate preventionBuild source to target reconciliation that isolates missing records, duplicates, and rejected rows, and pinpoints where a count mismatch occurred Data modeling Design and maintain dimensional models, defining fact table grain explicitly and preventing double counting across transaction and line level measuresImplement slowly changing dimensions, Type 1 and Type 2, including surrogate keys, effective dating, and retention of historical customer and product recordsDesign logical and physical models that support both analytics and operational reporting Data governance and security Implement data governance controls covering classification, lineage, data quality, and retentionBuild a PII classification system to identify, tag, and protect sensitive dataDesign and manage the data access layer, including users, roles, and permissions, with encryption, auditing, and compliance requirements Database administration and production support Administer production database environments for performance, reliability, availability, and securityTroubleshoot production issues, including failed loads, data discrepancies, capacity constraints, and performance bottlenecksTune queries, indexes, and configurations; manage backup, recovery, replication, and disaster recoveryDiagnose and resolve PySpark performance problems including data skew, partitioning, shuffle behavior, broadcast joins, and execution plan analysis Reporting and automation Automate data engineering and operational processes in PythonBuild and maintain dashboards and reports in Power BI, Tableau, or similar toolsPartner with Development, QA, and Operations on deployments, schema changes, and incident response What You Bring 5+ years in data engineering, ETL development, or data warehouse engineering, with recent hands on implementation workBachelor's degree in Computer Science, Information Systems, Data Engineering, or equivalent experienceExpert level Python for pipelines, automation, and operational tooling; production PySpark experience requiredDemonstrated experience implementing change data capture and incremental loading strategies, including cases where no reliable timestamp existsDemonstrated experience implementing slowly changing dimensions, surrogate keys, and dimensional models, and able to explain grain using real columns and keys from your own workExperience building reconciliation and data quality validation between source systems and targetsAdvanced SQL, including complex queries, query optimization, indexing, and execution plan analysisStrong working knowledge of relational databases, particularly PostgreSQL and Redshift, including schema design and performance tuningHands on data governance implementation experience, including access controls, classification, and lineageExperience supporting production database environments, including troubleshooting and incident responseExperience building dashboards and reports in Power BI, Tableau, or a comparable toolAbility to explain your own production implementations in detail, including what broke, what you changed, and why Preferred Experience with AWS data services such as S3, Glue, and Lambda; equivalent experience on other cloud platforms is welcome, as PySpark and modeling skills transferExperience migrating legacy ETL workloads, for example Informatica or SSIS, to modern cloud platformsExperience with governance tooling such as Unity Catalog, Purview, Collibra, or AlationBackground in a data intensive industry; energy sector experience is not required