Now Hiring: Data Engineer

All Climate First Bank employees must be willing to embrace the vision of an inclusive, equitable, and regenerative economic system. The Data Engineer designs, builds, maintains, and improves the Bank's enterprise data pipelines, integrations, databases, and analytical data models. The role owns the flow of data from source connection through ingestion, transformation, validation, storage, monitoring, and delivery to reporting and analytics consumers. This hands-on role spans cloud, on-premises, and hybrid environments. The Data Engineer integrates core banking platforms, operational databases, APIs, files, SaaS applications, and cloud data platforms while emphasizing secure, reliable, documented, and business-ready outcomes. The successful candidate must combine strong SQL, data integration, scripting, data quality, and troubleshooting skills with practical judgment and clear communication.

What You'll Contribute as a Data Engineer

Data Architecture and Pipeline Ownership

• Design, develop, operate, and improve end-to-end ETL and ELT pipelines from enterprise sources to business-ready datasets and analytical models.
• Own source connectivity, extraction, transformation, loading, validation, storage, monitoring, recovery, documentation, and operational support.
• Build maintainable solutions that meet defined business needs without unnecessary complexity; balance immediate delivery with a scalable foundation.
• Evaluate existing pipelines, rebuild unstable or obsolete processes, and select patterns based on business value, risk, cost, performance, security, and supportability

Data Integration and Source Connectivity

• Integrate Microsoft SQL Server and other relational databases, ODBC sources, REST APIs, SaaS applications, secure file locations, shared datasets, CSV, JSON, Parquet, and other approved sources.
• Develop reusable ingestion patterns for authentication, pagination, rate limits, token renewal, retries, logging, exception handling, and schema changes.
• Support batch, incremental, snapshot, near-real-time, and streaming patterns when justified by business requirements.
• Maintain source-to-target mappings and document how source fields become standardized, validated, and usable information.

SQL, Database Engineering, and Data Modeling

• Create complex SQL queries, views, stored procedures, functions, tables, keys, and transformation logic.
• Manage SQL Server from the database perspective, including schemas, objects, data structures, permissions coordination, backup requirements, troubleshooting, and performance.
• Design normalized structures and analytical models, including facts, dimensions, star schemas, data marts, and semantic-ready datasets.
• Optimize workloads through indexing, execution-plan analysis, partitioning, appropriate data types, incremental processing, and controlled historical retention.

ETL/ELT Development and Reliability

• Build and maintain workflows using approved platforms such as Azure Data Factory, SQL Server, and other Bank-approved tools.
• Determine when to use full loads, incremental loads, watermarks, change tracking, snapshots, or event-driven patterns.
• Create parameterized pipelines with retries, checkpoints, idempotency, dependency controls, backfill procedures, and failure notifications.
• Design fault-tolerant processing so isolated data errors are captured and reported without unnecessarily stopping unrelated workloads.

Data Quality, Validation, and Reconciliation

• Embed automated checks for schema, data types, required fields, ranges, reference values, row counts, totals, balances, and completeness.
• Detect and handle duplicates, null values, malformed identifiers, invalid formats, latearriving records, schema drift, and incorrect aggregates.
• Reconcile transformed data against authoritative source systems, control totals, approved reports, and business rules.
• Prevent unverified data from reaching production reporting layers; maintain traceability for exceptions, remediation, and reprocessing.

Cloud, Hybrid, Performance, and Cost

• Build and support data solutions across Microsoft Azure, Google Cloud Platform, and on-premises environments as required by the Bank's architecture.
• Securely move data between environments and coordinate on networking, integration runtimes, service accounts, secrets, access, and platform configuration.
• Improve execution time, query performance, freshness, reliability, and resource use through batching, parallelism, partitioning, workload management, and incremental processing.
• Assess storage, compute, movement, licensing, and duplication costs before changes; avoid unnecessary replication when secure direct-query or shared-data approaches are appropriate.

Data Consolidation and Big Data

• Be capable of creating and working with Big Data solutions and products, such as BigQuery.
• Consolidate pipelines results on centralized data stores such as Datalakes and others.

Analytics and Power BI Readiness

• Deliver clean, documented, trustworthy, and appropriately modeled data to Power BI and other approved reporting platforms.
• Build reusable enterprise datasets and semantic-ready models; support relationships, field definitions, refresh requirements, and upstream performance troubleshooting.
• Partner with analysts and reporting teams while maintaining clear ownership boundaries between engineering, analytical modeling, and visualization development.

Security, Governance, and Compliance

• Protect customer, financial, and operational information through least privilege, role-based access, encryption, approved secret management, logging, monitoring, and separation of duties.
• Maintain lineage, metadata, ownership, classification, retention, and access documentation; support audits, risk assessments, vendor reviews, and examinations.
• Ensure solutions align with Bank policies and applicable expectations, including GLBA, FFIEC, NIST, BSA, AML, CIP, and OFAC; escalate suspected control failures or unauthorized access.

Operations, Documentation, and Collaboration

• Monitor pipelines, integrations, databases, and delivery commitments; perform root-cause analysis and implement durable corrective actions.
• Maintain architecture and data-flow diagrams, entity relationships, dictionaries, pipeline inventories, transformation specifications, validation rules, runbooks, dependencies, and recovery procedures.
• Use approved shared repositories, version control, peer review, controlled releases, and change management for SQL, Python, pipeline assets, and documentation.
• Gather requirements and communicate scope, dependencies, risks, limitations, timelines, and recovery status clearly to technical teams, business users, executives, vendors, and auditors.

What You'll Leverage in this Role:

Experience & Education

• Bachelor's degree in Computer Science, Information Systems, Data Engineering, Software Engineering, Mathematics, Finance, or a related field; equivalent practical experience may be considered.
• 3+ years of relevant experience in data engineering, database development, ETL/ELT development, analytics engineering, or a related discipline.
• Demonstrated ownership of production data pipelines from source ingestion through a final analytical model or business-ready dataset.
• Experience in banking, financial services, healthcare, insurance, or another regulated industry is preferred.
• Experience with core systems migrations, data-platform modernization, reporting conversions, or foundational data-environment buildouts is preferred.

Required Technical Skills

• Advanced SQL and T-SQL, including complex joins, transformations, window functions, stored procedures, query tuning, indexing, schema design, and incremental patterns.
• Demonstrated experience with CRM data streams (modeling and integration), like Salesforce and DataHub.
• Microsoft SQL Server experience from a database and data-engineering perspective.
• Hands-on experience with Azure Data Factory or a directly comparable enterprise orchestration platform.
• Proficiency in Python or an equivalent language for ingestion, transformation, validation, automation, and troubleshooting.
• Experience with APIs, file-based data, hybrid integration, analytical modeling, automated quality controls, monitoring, recovery, Git-based version control, and production support.
• Working knowledge of Power BI data models, relationships, refresh patterns, and upstream performance considerations; understanding of access control, encryption, logging, and auditability.

We are unable to provide employment visa sponsorship now or in the future.

Entrepreneurial Self-Starter Mentality - You take charge of your work product and take pride in delivering consistently great and measurable results. You solve problems efficiently, seek operational efficiencies and are an analytical thinker with a strong focus on data-driven decision making. A high-pace and high-expectation environment excites you and you can't wait to push yourself to new professional heights in a company that rewards high performance.

Master Communicator - Whether it's in-person, on camera, phone, or chat - you communicate with confidence, precision, and professionalism. You listen deeply and respond thoughtfully and are able to engage efficiently and tactfully with stakeholders at all levels of the organization. Additionally, you masterfully build relationships and develop business with strong influencing and decision-making skills.

Banking & Fintech Acumen- You are highly educated in financial products and services, applicable regulations and laws. You also possess strong overall business acumen and the ability to interpret financial reports and legal documents. Strong knowledge of unique industries and markets in conjunction with a broad knowledge of business banking products and services is required.

Tech-Forward and Analytical Thinking - You learn new tools quickly, are excited by innovation and leverage technology to optimize your work product and streamline your creative process.

Commitment to Being a Team Player - You lift others up, share ideas, and bring positive energy to everything you do. You know what it ta