Data Engineer, Data Strategy Lead, Data Scientist

(1) Process Engineering / Data Pipelines: Collected and mapped out several workflows or
archictural concepts across the organization in Confluence, including CRM, invoicing,
techno-economic simulation, and voltage sag states for a power grid.

(2) Techno-Economic Analysis: Calculated first-order difference on MW-scale power requirements
to correct / improve upon legacy spreadmart in use cases targeted towards Signal Processing
(superset of GSP). Monthly profiles based on grid uptime with battery storage co-location
generated price tenors based on energy rates for consumption by business owners to surmise
feasibility of the solution.

(3) Presales Activities: Attended trade shows as-needed at Messukeskus in Finland and gauge
buyer interest through vendor portals such as SAP Ariba. Network with market professionals and
share project intelligence on data exchanges, intelligent automation, and other topics for Industry
4.0+.

(4)

(I) Market Intelligence: Consolidated RSS feeds for media monitoring and developed PoC for a
top-down view on renewable energy headlines for downstream sentiment analysis. Largely used
Draw.IO and Grafana for implementation.

(II) HubSpot Workflows: Generated process-based workflows using automations to curate our
database based on last activity date, duplicate records, and client vertical. Critical pre-processing
step for sales pipeline understanding and analysis during optimization steps.

(1) Industrial Analytics Portfolio: Participated in team-based workshops with Talouselämä 500
and EMEA customers, including Olvi, Ramirent, and ABB. Conducted comprehensive activities
such as functional analysis, requirements gathering, backlog refinement, and use case
identification. Provided C-level and IT stakeholders with tool recommendations, detailed




implementation risks assessments, and strategic data insights based on Kimball methodologies.
Developed boilerplate examples, including COGS, price effect, and revenue analysis, utilizing dbt
and SQL for efficient data modeling.

(2) Client engagements were an iterative process of collecting and validating business logic &
disparate micro-services: we offered reference architectures, coaching and clear explanations on
advanced topics such as Slowly-Changing Dimensions and Data Mesh. Utilized Ellie for modeling
concepts into diagrams and their constituent data catalogs, employing Star or Snowflake schema
for structuring data effectively. Sparred with stakeholders on benefits of tools such as Prefect vs.
Snowflake for master data management and harmonization.

(3) Project work included Demand Forecasting with Snowflake and dbt for testing Snowpark /
third-party Python libraries with streaming datasets. Responsible for data normalization and
cleansing using Azure Data Factory, ensuring data quality and integrity. Implemented task
scheduling with SQL to automate data processing workflows. Identified SOCKS5 vulnerabilities
and explored new functionality during RESTful network calls to [FINGRID] data provider,
enhancing security and functionality of data integration processes for JSON interchange.

(4) Played a direct key role in legacy migrations of customer databases written in ANSI-SQL to
dbt, leading to substantial improvements in reporting speed and statistical efficiency using
common-table expressions (CTEs). Focused on use cases targeted towards financial analysis and
IFRS® Standards, enhancing discounted cash-flow calculations and moving average analyses.
This migration effort significantly contributed to the operational efficiency and accuracy of financial
reporting.

(1) Faster data processing and OLTP techniques drove efficient data processing methods and
utilized OLTP for transforming datasets into actionable insights during our operations. This involved
leveraging database technologies and optimizing SQL queries for enhanced performance, aligning
with best practices for data management and analytics in the cloud.

(2) Grew client base from 0 to 400 by implementing database sanitization and Robotic Process
Automation (RPA), generating online leads at scale. This strategy not only expanded the client
base but also streamlined the process of lead generation, improving scalability.

(3) Developed BI dashboards using YouTube Analytics , tracking key metrics to prioritize
high-performing talent by seasonality/genre and increase advertising CPM by almost 2x. This
initiative leveraged data visualization tools and analytics platforms to optimize content strategy and
advertising spend, demonstrating proficiency in analytics and data-driven decision-making.

(4) Managed requirements gathering and outsourcing of Google Sheets widgets , improving
contract renewals and social media profile curation. This task involved extensive use of GCP
technologies, such as BigQuery, for data analysis and reporting, enhancing the efficiency of
data-driven decision-making processes.

(5) Conducted data analysis and decision support experiments to understand YouTube claims
volume, generating ideas for higher deal flow and targeted TikTok content with TypeScript / Angular
and through JSON. This work involved utilizing data analytics tools and platforms to analyze claims
data and identify opportunities for content optimization, showcasing a comprehensive approach to
data analysis and strategy development.