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.

Experience, qualification, and soft skills, have you got everything required to succeed in this opportunity Find out below.

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 S3
• Implement change data capture, including identifying changed records where source systems provide no reliable modification timestamp, using log based CDC, hash comparison, or snapshot differencing
• Design idempotent pipelines that restart cleanly after partial loads, with checkpoint management and duplicate prevention
• Build 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 measures
• Implement slowly changing dimensions, Type 1 and Type 2, including surrogate keys, effective dating, and retention of historical customer and product records
• Design 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 retention
• Build a PII classification system to identify, tag, and protect sensitive data
• Design 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 security
• Troubleshoot production issues, including failed loads, data discrepancies, capacity constraints, and performance bottlenecks
• Tune queries, indexes, and configurations; manage backup, recovery, replication, and disaster recovery
• Diagnose 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 Python
• Build and maintain dashboards and reports in Power BI, Tableau, or similar tools
• Partner 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 work
• Bachelor's degree in Computer Science, Information Systems, Data Engineering, or equivalent experience
• Expert level Python for pipelines, automation, and operational tooling; production PySpark experience required
• Demonstrated experience implementing change data capture and incremental loading strategies, including cases where no reliable timestamp exists
• Demonstrated experience implementing slowly changing dimensions, surrogate keys, and dimensional models, and able to explain grain using real columns and keys from your own work xhyhwjd
• Experience building reconciliation and data quality validation between source systems and targets
• Advanced SQL, including complex queries, query optimization, indexing, and execution plan analysis
• Strong working knowledge of relational databases, particularly PostgreSQL and Redshift, including schema design and performance tuning
• Hands on data governance implementation experience, including access controls, classification, and lineage
• Experience supporting production database environments, including troubleshooting and incident response
• Experience building dashboards and reports in Power BI, Tableau, or a comparable tool
• Ability 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 transfer
• Experience migrating legacy ETL workloads, for example Informatica or SSIS, to modern cloud platforms
• Experience with governance tooling such as Unity Catalog, Purview, Collibra, or Alation
• Background in a data intensive industry; energy sector experience is not required