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Sr. Data Engineer
Location : NYC - Park Ave (ON-SITE 3 days a week)
Salary: 135 - 150K + Target bonus of 7% + 1.5 Pension Plan

This is working for a very stable company that has never laid off people through recessions. They are a solid smaller bank that is continuing to grow.

Must have the following skills to be considered for this role:
1. SQL Server
2. Python - write/read/edit scripts
3. Conceptual understanding of Cloud Technologies
4. Person must be generally interested in tools. Always be open to learning new tools that could help the company. Proactive with tools.
5. Extroverted personality - Must be able to work with internal clients
6. ETL, ELT experience is a must.
7. Any knowledge of the following tools would be a plus: SnowFlake (Data Warehouse Cloud), 5Tran (pipe), DBT - Transformation Tool (run in SQL), ClickSense (a competitor to Tableau), and Trifacta (data wrangling tool) - (configuration and administer them). All SaaS tools. Any knowledge of how they operate, admin functions, etc would be a plus.

This person would be supporting a self-serve Data Analytics department that acts as a center of excellence for the company.

Responsible for maintaining a modern analytics tool stack, onboarding new data into the central data repository to engineer it for use by data consumers, configuring analytics environments that draw from the central data repository, creating data sandboxes for use with experimental data, and facilitating self-service data prep by data consumers.

Team Overview: The Data Management Office: identifies the Bank's data needs and aligns data investments with business strategy; partners with stakeholders to apply data governance best practices to activities with tangible business value; and rapidly & securely provisions the right data to the right audience at the right time.
Essential duties:

• Collaborate with the Bank's analytics community to identify, prioritize, and execute Enterprise Analytics activities that add demonstrable value to the business.
• Establish a steady and timely flow of work, on-boarding new and modified data sets into production within the Bank's central data repository.
• Maintain the technical health of the team's technologies, including but not limited to: system configuration: maintenance; testing: and installation of new components.
• Work in agile manner and own assigned tasks, making sure they are completed within the designated time frame.
• Contribute recommendations on innovative, efficient, and timely methods to accomplish data engineering goals.
• Administer the content and use of the technologies supporting Enterprise Analytics, including but not limited to: extract & load; transformations; end-user self-serve data preparation; data storage; data warehouse architecture; data warehouse compute optimization; and secure connections in & out of the environment.
• Collaborate with internal and external subject matter experts on the optimization of supported technologies.
• Create and maintain documentation on the Enterprise Analytics technology stack and its interdependencies with other Bank tools and processes (e.g. departmental data marts, data catalog, data quality & metadata management, information supply chains, lineage).
• Provide training to both data producers and data consumers on how to get the most value from the centralized data assets of the Bank and from the tools deployed to support them.
• Provide ad-hoc guidance on supported technologies to all users via any engagement type (e.g. in-person, telephone/email/chat outreach, service tickets) in a professional, helpful, and timely manner.

Other duties:

• Collaborate with other Data Management Office staff as needed to advance the maturity of the bank's overall data management.
• Identify existing and avoid building new roadblocks to delivering business value in a timely manner.

Education: Bachelor's degree or equivalent years of experience.

Experience:

• 7-10 years in a variety of data-centric development roles
• 10 years of experience in solving technical issues
• 4-7 years of experience in a customer-facing capacity (internal or external)
• Experience on the business/usage of data analytics is not required but is a plus

Interpersonal Skills:

• Excellent interpersonal skills and the ability to present technical and conceptual information concisely to the stakeholders from senior management to junior staff.
• Proactively solves problems and seeks to own the resolution of issues throughout their lifecycle.
• Takes critical feedback in stride and adjusts approaches accordingly.
• Acts as a point of help and excellent customer service.

Technical skills:

• Proven expertise in engineering data at any level of maturity (i.e. from raw to integrated to curated) in a data warehouse environment.
• Expert ability to detect patterns in data and to apply best practices to the design of appropriate database architecture, schemas, and constraints within an enterprise data warehouse environment.
• Proven expertise of data governance dependencies on an enterprise analytics platform, including data quality, metadata management, and data catalog.
• Solid knowledge of a modern data analytics tool stack that includes its administration and integration with other technologies, including schedulers (e.g. Tidal).
• Hands-on experience of working with multiple data ingestion patterns, (e.g. ETL, ELT, streaming, Change Data Capture (CDC), micro-batches, and data wrangling).
• Working knowledge of cloud-native approaches to data warehousing, including raw data storage, scalable data stores, elastic computing, and support for multiple marts.
• Hands-on experience in moving data from SQL and Oracle databases into Cloud.
• Experience with various data integration methods including APIs.
• Good understanding of process cataloging (e.g. Bitbucket).
• Experience with logical and physical data modeling with an industry-standard tool (e.g. Erwin).
• Experience with multiple data analysis patterns and relevant tools (e.g. canned reporting, visualization, and dashboards).
• Good understanding of information security and data privacy concepts (e.g. PII tokenization and sensitive data masking)
• Experience with agile (e.g. scrum, Kanban) and DevOps practices is a plus.
• Familiarity with the needs of data science/modeling use cases and relevant tools (e.g. Python, R) is a plus.
• Familiarity with the needs of data science/modeling use cases and relevant tools (e.g. Python, R) is a plus.

Credentials:
Data development-oriented certifications are not required but are a plus (e.g. Snowflake SnowPro, AWS Certified Big Data Specialist)

INDH
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