Social Media Data Engineer

NOTE: IF YOU HAVE NO EXPERIENCE AQUIRING BULK SOCIAL MEDIA DATA DO NOT APPLY

-Please explain your relevant experience clearly

About Deeter Investments

Deeter Investments is a founder-led proprietary trading firm built around real-time, data-driven decision-making. We prize curiosity, collaboration, and a bias for action. After years of discretionary success, we’re expanding our algorithmic division—and we’re hiring our first Social Media Data Analyst to turn the internet’s noise into tradable signal.

Role Description

You’ll be the point person for sourcing, cleaning, and interpreting social-media and web-native data (X/Twitter, Reddit, TikTok, YouTube, Discord/Telegram, major news, and niche forums). Your job: identify early moves, sentiment shifts, and “virality” patterns that matter for markets—and get those insights into traders’ hands fast via dashboards, alerts, and research-ready datasets.

This is a hands-on role: you’ll pull data from APIs/brokers, structure it, label it, score it, test what actually predicts price/volume, and ship lightweight tools that the team uses daily.

Key Responsibilities

· Data sourcing & hygiene: Acquire streams from platforms and data brokers; de-duplicate, de-spam, and defeat bots; maintain coverage maps and latency SLAs.

· Entity & ticker extraction: Build/maintain pipelines that correctly tag tickers, companies, and themes across slang, emojis, tickers-in-images, and misspellings.

· Signal & sentiment: Produce sentiment/stance scores, virality/acceleration metrics, influencer/graph features, and “surprise vs baseline” indicators.

· Quality & governance: Track precision/recall of extraction, false-positive rates, freshness, and source reliability; document assumptions and licensing/entitlements.

· Collaboration: Work directly with traders, quants, and data engineers to iterate quickly from idea → prototype → production.

Qualifications & Experience

· 3–6+ years in data analysis or applied analytics (content, social, growth, alt-data, or market intelligence).

· Strong Python and SQL; comfortable wrangling messy text/video-adjacent metadata and large timelines.

· Practical NLP toolkit (regex → embeddings/classifiers); able to explain tradeoffs in simple terms.

· Experience with social-platform APIs, third-party data brokers, or ethically compliant web ingestion.

· Solid statistics for backtests and A/B-style validation; know how to avoid obvious pitfalls (look-ahead bias, survivorship, data leakage).

· Communicates crisply: turns complex evidence into one-page briefs and clear “what to do” recommendations.

Nice to Have

· Markets familiarity (tickers, earnings, filings, corporate actions) and event-study workflows.

· Graph/“influence network” features, basic time-series modeling, or anomaly detection.

· Experience with columnar data and fast queries (Parquet/Delta/Iceberg; DuckDB/ClickHouse/BigQuery/Snowflake).

· Light multimodal experience (ASR/transcription, OCR for screenshots, basic image/video metadata).

Example Projects You Might Ship in Month 1–3

· A ticker-tagging & sentiment pipeline that posts concise, source-linked alerts to Slack when social momentum and news flow diverge.

· A virality tracker that scores acceleration vs. each ticker’s normal social baseline and shows 1-day/1-week outcomes.

· An event study template (CSV in/out + notebook) that any trader can run to test a new social signal in under 10 minutes.

What Success Looks Like

· Higher signal-to-noise for the desk; earlier heads-up on real catalysts; measurable lift in PnL attribution to social signals.

· Clear, reproducible metrics: coverage %, freshness (latency), precision/recall for tagging, and backtest effect sizes with confidence intervals.

Location: Remote (Europe time-zone overlap preferred)

Language: English

Employment: Full-time